Tag Archives: Machine Intelligence

Discovering Anomalous Data with Self-Supervised Learning

Anomaly detection (sometimes called outlier detection or out-of-distribution detection) is one of the most common machine learning applications across many domains, from defect detection in manufacturing to fraudulent transaction detection in finance. It is most often used when it is easy to collect a large amount of known-normal examples but where anomalous data is rare and difficult to find. As such, one-class classification, such as one-class support vector machine (OC-SVM) or support vector data description (SVDD), is particularly relevant to anomaly detection because it assumes the training data are all normal examples, and aims to identify whether an example belongs to the same distribution as the training data. Unfortunately, these classical algorithms do not benefit from the representation learning that makes machine learning so powerful. On the other hand, substantial progress has been made in learning visual representations from unlabeled data via self-supervised learning, including rotation prediction and contrastive learning. As such, combining one-class classifiers with these recent successes in deep representation learning is an under-explored opportunity for the detection of anomalous data.

In “Learning and Evaluating Representations for Deep One-class Classification”, presented at ICLR 2021, we outline a 2-stage framework that makes use of recent progress on self-supervised representation learning and classic one-class algorithms. The algorithm is simple to train and results in state-of-the-art performance on various benchmarks, including CIFAR, f-MNIST, Cat vs Dog and CelebA. We then follow up on this in “CutPaste: Self-Supervised Learning for Anomaly Detection and Localization”, presented at CVPR 2021, in which we propose a new representation learning algorithm under the same framework for a realistic industrial defect detection problem. The framework achieves a new state-of-the-art on the MVTec benchmark.

A Two-Stage Framework for Deep One-Class Classification
While end-to-end learning has demonstrated success in many machine learning problems, including deep learning algorithm designs, such an approach for deep one-class classifiers often suffer from degeneration in which the model outputs the same results regardless of the input.

To combat this, we apply a two stage framework. In the first stage, the model learns deep representations with self-supervision. In the second stage, we adopt one-class classification algorithms, such as OC-SVM or kernel density estimator, using the learned representations from the first stage. This 2-stage algorithm is not only robust to degeneration, but also enables one to build more accurate one-class classifiers. Furthermore, the framework is not limited to specific representation learning and one-class classification algorithms — that is, one can easily plug-and-play different algorithms, which is useful if any advanced approaches are developed.

A deep neural network is trained to generate the representations of input images via self-supervision. We then train one-class classifiers on the learned representations.

Semantic Anomaly Detection
We test the efficacy of our 2-stage framework for anomaly detection by experimenting with two representative self-supervised representation learning algorithms, rotation prediction and contrastive learning.

Rotation prediction refers to a model’s ability to predict the rotated angles of an input image. Due to its promising performance in other computer vision applications, the end-to-end trained rotation prediction network has been widely adopted for one-class classification research. The existing approach typically reuses the built-in rotation prediction classifier for learning representations to conduct anomaly detection, which is suboptimal because those built-in classifiers are not trained for one-class classification.

In contrastive learning, a model learns to pull together representations from transformed versions of the same image, while pushing representations of different images away. During training, as images are drawn from the dataset, each is transformed twice with simple augmentations (e.g., random cropping or color changing). We minimize the distance of the representations from the same image to encourage consistency and maximize the distance between different images. However, usual contrastive learning converges to a solution where all the representations of normal examples are uniformly spread out on a sphere. This is problematic because most of the one-class algorithms determine the outliers by checking the proximity of a tested example to the normal training examples, but when all the normal examples are uniformly distributed in an entire space, outliers will always appear close to some normal examples.

To resolve this, we propose distribution augmentation (DA) for one-class contrastive learning. The idea is that instead of learning representations from the training data only, the model learns from the union of the training data plus augmented training examples, where the augmented examples are considered to be different from the original training data. We employ geometric transformations, such as rotation or horizontal flip, for distribution augmentation. With DA, the training data is no longer uniformly distributed in the representation space because some areas are occupied by the augmented data.

Left: Illustrated examples of perfect uniformity from the standard contrastive learning. Right: The reduced uniformity by the proposed distribution augmentation (DA), where the augmented data occupy the space to avoid the uniform distribution of the inlier examples (blue) throughout the whole sphere.

We evaluate the performance of one-class classification in terms of the area under receiver operating characteristic curve (AUC) on the commonly used datasets in computer vision, including CIFAR10 and CIFAR-100, Fashion MNIST, and Cat vs Dog. Images from one class are given as inliers and those from remaining classes are given as outliers. For example, we see how well cat images are detected as anomalies when dog images are inliers.

   CIFAR-10       CIFAR-100       f-MNIST       Cat v.s. Dog   
Ruff et al. (2018) 64.8 - - -
Golan and El-Yaniv (2018) 86.0 78.7 93.5 88.8
Bergman and Hoshen (2020) 88.2 - 94.1 -
Hendrycks et al. (2019) 90.1 - - -
Huang et al. (2019) 86.6 78.8 93.9 -
2-stage framework: rotation prediction    91.3±0.3 84.1±0.6 95.8±0.3 86.4±0.6
2-stage framework: contrastive (DA) 92.5±0.6 86.5±0.7 94.8±0.3 89.6±0.5
Performance comparison of one-class classification methods. Values are the mean AUCs and their standard deviation over 5 runs. AUC ranges from 0 to 100, where 100 is perfect detection.

Given the suboptimal built-in rotation prediction classifiers typically used for rotation prediction approaches, it’s notable that simply replacing the built-in rotation classifier used in the first stage for learning representations with a one-class classifier at the second stage of the proposed framework significantly boosts the performance, from 86 to 91.3 AUC. More generally, the 2-stage framework achieves state-of-the-art performance on all of the above benchmarks.

With classic OC-SVM, which learns the area boundary of representations of normal examples, the 2-stage framework results in higher performance than existing works as measured by image-level AUC.

Texture Anomaly Detection for Industrial Defect Detection
In many real-world applications of anomaly detection, the anomaly is often defined by localized defects instead of entirely different semantics (i.e., being different in general). For example, the detection of texture anomalies is useful for detecting various kinds of industrial defects.

The examples of semantic anomaly detection and defect detection. In semantic anomaly detection, the inlier and outlier are different in general, (e.g., one is a dog, the other a cat). In defect detection, the semantics for inlier and outlier are the same (e.g., they are both tiles), but the outlier has a local anomaly.

While learning representations with rotation prediction and distribution-augmented contrastive learning have demonstrated state-of-the-art performance on semantic anomaly detection, those algorithms do not perform well on texture anomaly detection. Instead, we explored different representation learning algorithms that better fit the application.

In our second paper, we propose a new self-supervised learning algorithm for texture anomaly detection. The overall anomaly detection follows the 2-stage framework, but the first stage, in which the model learns deep image representations, is specifically trained to predict whether the image is augmented via a simple CutPaste data augmentation. The idea of CutPaste augmentation is simple — a given image is augmented by randomly cutting a local patch and pasting it back to a different location of the same image. Learning to distinguish normal examples from CutPaste-augmented examples encourages representations to be sensitive to local irregularity of an image.

The illustration of learning representations by predicting CutPaste augmentations. Given an example, the CutPaste augmentation crops a local patch, then pasties it to a randomly selected area of the same image. We then train a binary classifier to distinguish the original image and the CutPaste augmented image.

We use MVTec, a real-world defect detection dataset with 15 object categories, to evaluate the approach above.

  DOCC
(Ruff et al., 2020)  
  U-Student
(Bergmann et al., 2020)  
  Rotation Prediction     Contrastive (DA)     CutPaste  
87.9 92.5 86.3 86.5 95.2
Image-level anomaly detection performance (in AUC) on the MVTec benchmark.

Besides image-level anomaly detection, we use the CutPaste method to locate where the anomaly is, i.e., “patch-level” anomaly detection. We aggregate the patch anomaly scores via upsampling with Gaussian smoothing and visualize them in heatmaps that show where the anomaly is. Interestingly, this provides decently improved localization of anomalies. The below table shows the pixel-level AUC for localization evaluation.

  Autoencoder
(Bergmann et al., 2019)  
  FCDD
(Ruff et al., 2020)  
  Rotation Prediction     Contrastive (DA)     CutPaste  
86.0 92.0 93.0 90.4 96.0
Pixel-level anomaly localization performance (in AUC) comparison between different algorithms on the MVTec benchmark.

Conclusion
In this work we introduce a novel 2-stage deep one-class classification framework and emphasize the importance of decoupling building classifiers from learning representations so that the classifier can be consistent with the target task, one-class classification. Moreover, this approach permits applications of various self-supervised representation learning methods, attaining state-of-the-art performance on various applications of visual one-class classification from semantic anomaly to texture defect detection. We are extending our efforts to build more realistic anomaly detection methods under the scenario where training data is truly unlabeled.

Acknowledgements
We gratefully acknowledge the contribution from other co-authors, including Jinsung Yoon, Minho Jin and Tomas Pfister. We release the code in our GitHub repository.

Source: Google AI Blog


RxR: A Multilingual Benchmark for Navigation Instruction Following

A core challenge in machine learning (ML) is to build agents that can navigate complex human environments in response to spoken or written commands. While today’s agents, including robots, can often navigate complicated environments, they cannot yet understand navigation goals expressed in natural language, such as, “Go past the brown double doors that are closed to your right and stand behind the chair at the head of the table.”

This challenge, referred to as vision-and-language navigation (VLN), demands a sophisticated understanding of spatial language. For example, the ability to identify the position “behind the chair at the head of the table requires finding the table, identifying which part of the table is considered to be the “head”, finding the chair closest to the head, identifying the area behind this chair and so on. While people can follow these instructions easily, these challenges cannot be easily solved with current ML-based methods, requiring systems that can better connect language to the physical world it describes.

To help spur progress in this area, we are excited to introduce Room-Across-Room (RxR), a new dataset for VLN. Described in “Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding”, RxR is the first multilingual dataset for VLN, containing 126,069 human-annotated navigation instructions in three typologically diverse languages — English, Hindi and Telugu. Each instruction describes a path through a photorealistic simulator populated with indoor environments from the Matterport3D dataset, which includes 3D captures of homes, offices and public buildings. To track progress on VLN, we are also announcing the RxR Challenge, a competition that encourages the machine learning community to train and evaluate their own instruction following agents on RxR instructions.

Language Instruction
en-US Starting next to the long dining room table, turn so the table is to your right. Walk towards the glass double doors. When you reach the mat before the doors, turn immediately left and walk down the stairs. When you reach the bottom of the stairs, walk through the open doors to your left and continue through the art exhibit with the tub to your right hand side. Down the length of the table until you reach the small step at the end of the room before you reach the tub and stop.
  
hi-IN अभी हमारे बायीं ओर एक बड़ा मेज़ है कुछ कुर्सियाँ हैं और कुछ दीपक मेज़ के ऊपर रखे हैं। उलटी दिशा में घूम जाएँ और सिधा चलें। अभी हमारे दायीं ओर एक गोल मेज़ है वहां से सीधा बढ़ें और सामने एक शीशे का बंद दरवाज़ा है उससे पहले बायीं ओर एक सीढ़ी है उससे निचे उतरें। निचे उतरने के बाद दायीं ओर मुड़े और एक भूरे रंग के दरवाज़े से अंदर प्रवेश करें और सीधा चलें। अभी हमारे दायीं ओर एक बड़ा मेज़ है और दो कुर्सियां राखी हैं सीधा आगे बढ़ें। हमारे सामने एक पानी का कल है और सामने तीन कुर्सियां दिवार के पास रखी हैं यहीं पर ठहर जाएँ।
  
te-IN ఉన్న చోటు నుండి వెనకకు తిరిగి, నేరుగా వెళ్తే, మీ ముందర ఒక బల్ల ఉంటుంది. దాన్ని దాటుకొని ఎడమవైపుకి తిరిగితే, మీ ముందర మెట్లు ఉంటాయి. వాటిని పూర్తిగా దిగండి. ఇప్పుడు మీ ముందర రెండు తెరిచిన ద్వారాలు ఉంటాయి. ఎడమవైపు ఉన్న ద్వారం గుండా బయటకు వెళ్ళి, నేరుగా నడవండి. ఇప్పుడు మీ కుడివైపున పొడవైన బల్ల ఉంటుంది. దాన్ని దాటుకొని ముందరే ఉన్న మెట్ల వద్దకు వెళ్ళి ఆగండి.

Examples of English, Hindi and Telugu navigation instructions from the RxR dataset. Each navigation instruction describes the same path.

Pose Traces
In addition to navigation instructions and paths, RxR also includes a new, more detailed multimodal annotation called a pose trace. Inspired by the mouse traces captured in the Localized Narratives dataset, pose traces provide dense groundings between language, vision and movement in a rich 3D setting. To generate navigation instructions, we ask guide annotators to move along a path in the simulator while narrating the path based on the surroundings. The pose trace is a record of everything the guide sees along the path, time-aligned with the words in the navigation instructions. These traces are then paired with pose traces from follower annotators, who are tasked with following the intended path by listening to the guide’s audio, thereby validating the quality of the navigation instructions. Pose traces implicitly capture notions of landmark selection and visual saliency, and represent a play-by-play account of how to solve the navigation instruction generation task (for guides) and the navigation instruction following task (for followers).

Example English navigation instruction in the RxR dataset. Words in the instruction text (right) are color-coded to align with the pose trace (left) that illustrates the movements and visual percepts of the guide annotator as they move through the environment describing the path.
The same RxR example with words in the navigation instruction aligned to 360° images along the path. The parts of the scene the guide annotator observed are highlighted; parts of the scene ignored by the annotator are faded. Red and yellow boxes highlight some of the close alignments between the textual instructions and the annotator's visual cues. The red cross indicates the next direction the annotator moved.

Scale
In total, RxR contains almost 10 million words, making it around 10 times larger than existing datasets, such as R2R and Touchdown/Retouchdown. This is important because, in comparison to tasks based on static image and text data, language tasks that require learning through movement or interaction with an environment typically suffer from a lack of large-scale training data. RxR also addresses known biases in the construction of the paths that have arisen in other datasets, such as R2R in which all paths have similar lengths and take the shortest route to the goal. In contrast, the paths in RxR are on average longer and less predictable, making them more challenging to follow and encouraging models trained on the dataset to place greater emphasis on the role of language in the task. The size, scope and detail of RxR will expand the frontier for research on grounded language learning while reducing the dominance of high resource languages such as English.

Left: RxR is an order of magnitude larger than similar existing datasets. Right: Compared to R2R, the paths in RxR are typically longer and less predictable, making them more challenging to follow.

Baselines
To better characterize and understand the RxR dataset, we trained a variety of agents on RxR using our open source framework VALAN, and language representations from the multilingual BERT model. We found that results were improved by including follower annotations as well as guide annotations during training, and that independently trained monolingual agents outperformed a single multilingual agent.

Conceptually, evaluation of these agents is straightforward — did the agent follow the intended path? Empirically, we measure the similarity between the path taken by the VLN agent and the reference path using NDTW, a normalized measure of path fidelity that ranges between 100 (perfect correspondence) and 0 (completely wrong). The average score for the follower annotators across all three languages is 79.5, due to natural variation between similar paths. In contrast, the best model (a composite of three independently trained monolingual agents, one for each language) achieved an NDTW score on the RxR test set of 41.5. While this is much better than random (15.4), it remains far below human performance. Although advances in language modeling continue to rapidly erode the headroom for improvement in text-only language understanding benchmarks such as GLUE and SuperGLUE, benchmarks like RxR that connect language to the physical world offer substantial room for improvement.

Results for our multilingual and monolingual instruction following agents on the RxR test-standard split. While performance is much better than a random walk, there remains considerable headroom to reach human performance on this task.

Competition
To encourage further research in this area, we are launching the RxR Challenge, an ongoing competition for the machine learning community to develop computational agents that can follow natural language navigation instructions. To take part, participants upload the navigation paths taken by their agent in response to the provided RxR test instructions. In the most difficult setting (reported here and in the paper), all the test environments are previously unseen. However, we also allow for settings in which the agent is either trained in or explores the test environments in advance. For more details and the latest results please visit the challenge website.

PanGEA
We are also releasing the custom web-based annotation tool that we developed to collect the RxR dataset. The Panoramic Graph Environment Annotation toolkit (PanGEA), is a lightweight and customizable codebase for collecting speech and text annotations in panoramic graph environments, such as Matterport3D and StreetLearn. It includes speech recording and virtual pose tracking, as well as tooling to align the resulting pose trace with a manual transcript. For more details please visit the PanGEA github page.

Acknowledgements
The authors would like to thank Roma Patel, Eugene Ie and Jason Baldridge for their contributions to this research. We would also like to thank all the annotators, Sneha Kudugunta for analyzing the Telugu annotations, and Igor Karpov, Ashwin Kakarla and Christina Liu for their tooling and annotation support for this project, Austin Waters and Su Wang for help with image features, and Daphne Luong for executive support for the data collection.

Source: Google AI Blog


Estimating the Impact of Training Data with Reinforcement Learning

Recent work suggests that not all data samples are equally useful for training, particularly for deep neural networks (DNNs). Indeed, if a dataset contains low-quality or incorrectly labeled data, one can often improve performance by removing a significant portion of training samples. Moreover, in cases where there is a mismatch between the train and test datasets (e.g., due to difference in train and test location or time), one can also achieve higher performance by carefully restricting samples in the training set to those most relevant for the test scenario. Because of the ubiquity of these scenarios, accurately quantifying the values of training samples has great potential for improving model performance on real-world datasets.


Top: Examples of low-quality samples (noisy/crowd-sourced); Bottom: Examples of a train and test mismatch.

In addition to improving model performance, assigning a quality value to individual data can also enable new use cases. It can be used to suggest better practices for data collection, e.g., what kinds of additional data would benefit the most, and can be used to construct large-scale training datasets more efficiently, e.g., by web searching using the labels as keywords and filtering out less valuable data.

In “Data Valuation Using Deep Reinforcement Learning”, accepted at ICML 2020, we address the challenge of quantifying the value of training data using a novel approach based on meta-learning. Our method integrates data valuation into the training procedure of a predictor model that learns to recognize samples that are more valuable for the given task, improving both predictor and data valuation performance. We have also launched four AI Hub Notebooks that exemplify the use cases of DVRL and are designed to be conveniently adapted to other tasks and datasets, such as domain adaptationcorrupted sample discovery and robust learningtransfer learning on image data and data valuation.

Quantifying the Value of Data
Not all data are equal for a given ML model — some have greater relevance for the task at hand or are more rich in informative content than others. So how does one evaluate the value of a single datum? At the granularity of a full dataset, it is straightforward; one can simply train a model on the entire dataset and use its performance on a test set as its value. However, estimating the value of a single datum is far more difficult, especially for complex models that rely on large-scale datasets, because it is computationally infeasible to re-train and re-evaluate a model on all possible subsets.

To tackle this, researchers have explored permutation-based methods (e.g., influence functions), and game theory-based methods (e.g., data Shapley). However, even the best current methods are far from being computationally feasible for large datasets and complex models, and their data valuation performance is limited. Concurrently, meta learning-based adaptive weight assignment approaches have been developed to estimate the weight values using a meta-objective. But rather than prioritizing learning from high value data samples, their data value mapping is typically based on gradient descent learning or other heuristic approaches that alter the conventional predictor model training dynamics, which can result in performance changes that are unrelated to the value of individual data points.

Data Valuation Using Reinforcement Learning (DVRL)
To infer the data values, we propose a data value estimator (DVE) that estimates data values and selects the most valuable samples to train the predictor model. This selection operation is fundamentally non-differentiable and thus conventional gradient descent-based methods cannot be used. Instead, we propose to use reinforcement learning (RL) such that the supervision of the DVE is based on a reward that quantifies the predictor performance on a small (but clean) validation set. The reward guides the optimization of the policy towards the action of optimal data valuation, given the state and input samples. Here, we treat the predictor model learning and evaluation framework as the environment, a novel application scenario of RL-assisted machine learning.

Training with Data Value Estimation using Reinforcement Learning (DVRL). When training the data value estimator with an accuracy reward, the most valuable samples (denoted with green dots) are used more and more, whereas the least valuable samples (red dots) are used less frequently.

Results
We evaluate the data value estimation quality of DVRL on multiple types of datasets and use cases.

  • Model performance after removing high/low value samples
    Removing low value samples from the training dataset can improve the predictor model performance, especially in the cases where the training dataset contains corrupted samples. On the other hand, removing high value samples, especially if the dataset is small, decreases the performance significantly. Overall, the performance after removing high/low value samples is a strong indicator for the quality of data valuation.
    Accuracy with the removal of most and least valuable samples, where 20% of the labels are noisy by design. By removing such noisy labels as the least valuable samples, a high-quality data valuation method achieves better accuracy. We demonstrate that DVRL outperforms other methods significantly from this perspective.
    DVRL shows the fastest performance degradation after removing the most important samples and the slowest performance degradation after removing the least important samples in most cases, underlining the superiority of DVRL in identifying noisy labels compared to competing methods (Leave-One-Out and Data Shapley).

  • Robust learning with noisy labels
    We consider how reliably DVRL can learn with noisy data in an end-to-end way, without removing the low-value samples. Ideally, noisy samples should get low data values as DVRL converges and a high performance model would be returned.
    Robust learning with noisy labels. Test accuracy for ResNet-32 and WideResNet-28-10 on CIFAR-10 and CIFAR-100 datasets with 40% of uniform random noise on labels. DVRL outperforms other popular methods that are based on meta-learning.
    We show state-of-the-art results with DVRL in minimizing the impact of noisy labels. These also demonstrate that DVRL can scale to complex models and large-scale datasets.

  • Domain adaptation
    We consider the scenario where the training dataset comes from a substantially different distribution from the validation and testing datasets. Data valuation is expected to be beneficial for this task by selecting the samples from the training dataset that best match the distribution of the validation dataset. We focus on the three cases: (1) a training set based on image search results (low-quality web-scraped) applied to the task of predicting skin lesion classification using HAM 10000 data (high-quality medical); (2) an MNIST training set for a digit recognition task on USPS data (different visual domain); (3) e-mail spam data to detect spam applied to an SMS dataset (different task). DVRL yields significant improvements for domain adaptation, by jointly optimizing the data valuator and corresponding predictor model.

Conclusions
We propose a novel meta learning framework for data valuation which determines how likely each training sample will be used in training of the predictor model. Unlike previous works, our method integrates data valuation into the training procedure of the predictor model, allowing the predictor and DVE to improve each other's performance. We model this data value estimation task using a DNN trained through RL with a reward obtained from a small validation set that represents the target task performance. In a computationally-efficient way, DVRL can provide high quality ranking of training data that is useful for domain adaptation, corrupted sample discovery and robust learning. We show that DVRL significantly outperforms alternative methods on diverse types of tasks and datasets.

Acknowledgements
We gratefully acknowledge the contributions of Tomas Pfister.

Source: Google AI Blog


Announcing the 7th Fine-Grained Visual Categorization Workshop



Fine-grained visual categorization refers to the problem of distinguishing between images of closely related entities, e.g., a monarch butterfly (Danaus plexippus) from a viceroy (Limenitis archippus). At the time of the first FGVC workshop in 2011, very few fine-grained datasets existed, and the ones that were available (e.g., the CUB dataset of 200 bird species, launched at that workshop) presented a formidable challenge to the leading classification algorithms of the time. Fast forward to 2020, and the computer vision landscape has undergone breathtaking changes. Deep learning based methods helped CUB-200-2011 accuracy rocket from 17% to 90% and fine-grained datasets have proliferated, with data arriving from a diverse array of institutions, such as art museums, apparel retailers, and cassava farms.

In order to help support even further progress in this field, we are excited to sponsor and co-organize the 7th Workshop on Fine-Grained Visual Categorization (FGVC7), which will take place as a virtual gathering on June 19, 2020, in conjunction with the IEEE conference on Computer Vision and Pattern Recognition (CVPR). We’re excited to highlight this year’s world-class lineup of fine-grained challenges, ranging from fruit tree disease prediction to fashion attributes, and we invite computer vision researchers from across the world to participate in the workshop.
The FGVC workshop at CVPR 2020 focuses on subordinate categories, including (from left to right) wildlife camera traps, plant pathology, birds, herbarium sheets, apparel, and museum artifacts.
Real-World Impact of the FGVC Challenges
In addition to pushing the frontier of fine-grained recognition on ever more challenging datasets, each FGVC workshop cycle provides opportunities for fostering new collaborations between researchers and practitioners. Some of the efforts from the FGVC workshop have made the leap into the hands of real world users.

The 2018 FGVC workshop hosted a Fungi challenge with data for 1,500 mushroom species provided by the Danish Mycological Society. When the competition concluded, the leaderboard was topped by a team from Czech Technical University and the University of West Bohemia.

The mycologists subsequently invited the Czech researchers for a visit to Copenhagen to explore further collaboration and field test a new workflow for collaborative machine learning research in biodiversity. This resulted in a jointly authored conference paper, a mushroom recognition app for Android and iOS, and an open access model published on TensorFlow Hub.
The Svampeatlas app for mushroom recognition is a result of a Danish-Czech collaboration spun out of the FGVC 2018 Fungi challenge. The underlying model is now published on TF Hub. Images used with permission of the Danish Mycological Society.
The iCassava Disease Challenge from 2019 mentioned above is another example of an FGVC team effort finding its way into the real world. In this challenge, Google researchers in Ghana collaborated with Makerere University and the National Crops Resources Research Institute (NaCRRI) to produce an annotated dataset of five cassava disease categories.
Examples of cassava leaf disease represented in the 2019 iCassava challenge.
The teams are testing a new model in the fields in Uganda with local farmers, and the model will be published on TFHub soon.

This Year’s Challenges
FGVC7 will feature six challenges, four of which represent sequels to past offerings, and two of which are brand new.

In iWildCam, the challenge is to identify different species of animals in camera trap images. Like its predecessors in 2018 and 2019, this year’s competition makes use of data from static, motion-triggered cameras used by biologists to study animals in the wild. Participants compete to build models that address diverse regions from around the globe, with a focus on generalization to held-out camera deployments within those regions, which exhibit differences in device model, image quality, local environment, lighting conditions, and species distributions, making generalization difficult.

It has been shown that species classification performance can be dramatically improved by using information beyond the image itself. In addition, since an ecosystem can be monitored in a variety of ways (e.g., camera traps, citizen scientists, remote sensing), each of which has its own strengths and limitations, it is important to facilitate the exploration of techniques for combining these complementary modalities. To this end, the competition provides a time series of remote sensing imagery for each camera trap location, as well as images from the iNaturalist competition datasets for species in the camera trap data.
Side-by-side comparison of image quality from iWildcam, captured from wildlife camera traps, (left) and iNaturalist (right), captured by conventional cameras. Images are from the 2020 iWildCam Challenge, and the iNaturalist competition datasets from 2017 and 2018.
The Herbarium Challenge, now in its second year, entails plant species identification, based on a large, long-tailed collection of herbarium specimens. Developed in collaboration with the New York Botanical Garden (NYBG), this challenge features over 1 million images representing over 32,000 plant species. Last year’s challenge was based on 46,000 specimens for 680 species. Being able to recognize species from historical herbarium collections can not only help botanists better understand changes in plant life on our planet, but also offers a unique opportunity to identify previously undescribed new species in the collection.
Representative examples of specimens from the 2020 Herbarium challenge. Images used with permission of the New York Botanical Garden.
In this year’s iMat Fashion challenge, participants compete to perform apparel instance segmentation and fine-grained attribute classification. The goal of this competition is to push the state of the art in fine-grained segmentation by joining forces between the fashion and computer vision communities. This challenge is in its third iteration, growing both in size and level of detail over past years’ offerings.

The last of the sequels is iMet, in which participants are challenged with building algorithms for fine-grained attribute classification on works of art. Developed in collaboration with the Metropolitan Museum of Art, the dataset has grown significantly since the 2019 edition, with a wide array of new cataloguing information generated by subject matter experts including multiple object classifications, artist, title, period, date, medium, culture, size, provenance, geographic location, and other related museum objects within the Met’s collection.

Semi-Supervised Aves is one of the new challenges at this year’s workshop. While avian data from iNaturalist has featured prominently in past FGVC challenges, this challenge focuses on the problem of learning from partially labeled data, a form of semi-supervised learning. The dataset is designed to expose some of the challenges encountered in realistic settings, such as the fine-grained similarity between classes, significant class imbalance, and domain mismatch between the labeled and unlabeled data.

Rounding out the set of challenges is Plant Pathology. In this challenge, the participants attempt to spot foliar diseases of apples using a reference dataset of expert-annotated diseased specimens. While this particular challenge is new to the FGVC community, it is the second such challenge to involve plant disease, the first being iCassava at last year’s FGVC.

Invitation to Participate
The results of these competitions will be presented at the FGVC7 workshop by top performing teams. We invite researchers, practitioners, and domain experts to participate in the FGVC workshop to learn more about state-of-the-art advances in fine-grained image recognition. We are excited to encourage the community's development of cutting edge algorithms for fine-grained visual categorization and foster new collaborations with global impact!

Acknowledgements
We’d like to thank our colleagues and friends on the FGVC7 organizing committee for working together to advance this important area. At Google we would like to thank Hartwig Adam, Kiat Chuan Tan, Arvi Gjoka, Kimberly Wilber, Sara Beery, Mikhail Sirotenko, Denis Brulé, Timnit Gebru, Ernest Mwebaze, Wojciech Sirko, Maggie Demkin.

Source: Google AI Blog


Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer



Over the past few years, transfer learning has led to a new wave of state-of-the-art results in natural language processing (NLP). Transfer learning's effectiveness comes from pre-training a model on abundantly-available unlabeled text data with a self-supervised task, such as language modeling or filling in missing words. After that, the model can be fine-tuned on smaller labeled datasets, often resulting in (far) better performance than training on the labeled data alone. The recent success of transfer learning was ignited in 2018 by GPT, ULMFiT, ELMo, and BERT, and 2019 saw the development of a huge diversity of new methods like XLNet, RoBERTa, ALBERT, Reformer, and MT-DNN. The rate of progress in the field has made it difficult to evaluate which improvements are most meaningful and how effective they are when combined.

In “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”, we present a large-scale empirical survey to determine which transfer learning techniques work best and apply these insights at scale to create a new model that we call the Text-To-Text Transfer Transformer (T5). We also introduce a new open-source pre-training dataset, called the Colossal Clean Crawled Corpus (C4). The T5 model, pre-trained on C4, achieves state-of-the-art results on many NLP benchmarks while being flexible enough to be fine-tuned to a variety of important downstream tasks. In order for our results to be extended and reproduced, we provide the code and pre-trained models, along with an easy-to-use Colab Notebook to help get started.

A Shared Text-To-Text Framework
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.
Diagram of our text-to-text framework. Every task we consider uses text as input to the model, which is trained to generate some target text. This allows us to use the same model, loss function, and hyperparameters across our diverse set of tasks including translation (green), linguistic acceptability (red), sentence similarity (yellow), and document summarization (blue). It also provides a standard testbed for the methods included in our empirical survey.
A Large Pre-training Dataset (C4)
An important ingredient for transfer learning is the unlabeled dataset used for pre-training. To accurately measure the effect of scaling up the amount of pre-training, one needs a dataset that is not only high quality and diverse, but also massive. Existing pre-training datasets don’t meet all three of these criteria — for example, text from Wikipedia is high quality, but uniform in style and relatively small for our purposes, while the Common Crawl web scrapes are enormous and highly diverse, but fairly low quality.

To satisfy these requirements, we developed the Colossal Clean Crawled Corpus (C4), a cleaned version of Common Crawl that is two orders of magnitude larger than Wikipedia. Our cleaning process involved deduplication, discarding incomplete sentences, and removing offensive or noisy content. This filtering led to better results on downstream tasks, while the additional size allowed the model size to increase without overfitting during pre-training. C4 is available through TensorFlow Datasets.

A Systematic Study of Transfer Learning Methodology
With the T5 text-to-text framework and the new pre-training dataset (C4), we surveyed the vast landscape of ideas and methods introduced for NLP transfer learning over the past few years. The full details of the investigation can be found in our paper, including experiments on:
  • model architectures, where we found that encoder-decoder models generally outperformed "decoder-only" language models;
  • pre-training objectives, where we confirmed that fill-in-the-blank-style denoising objectives (where the model is trained to recover missing words in the input) worked best and that the most important factor was the computational cost;
  • unlabeled datasets, where we showed that training on in-domain data can be beneficial but that pre-training on smaller datasets can lead to detrimental overfitting;
  • training strategies, where we found that multitask learning could be close to competitive with a pre-train-then-fine-tune approach but requires carefully choosing how often the model is trained on each task;
  • and scale, where we compare scaling up the model size, the training time, and the number of ensembled models to determine how to make the best use of fixed compute power.
Insights + Scale = State-of-the-Art
To explore the current limits of transfer learning for NLP, we ran a final set of experiments where we combined all of the best methods from our systematic study and scaled up our approach with Google Cloud TPU accelerators. Our largest model had 11 billion parameters and achieved state-of-the-art on the GLUE, SuperGLUE, SQuAD, and CNN/Daily Mail benchmarks. One particularly exciting result was that we achieved a near-human score on the SuperGLUE natural language understanding benchmark, which was specifically designed to be difficult for machine learning models but easy for humans.

Extensions
T5 is flexible enough to be easily modified for application to many tasks beyond those considered in our paper, often with great success. Below, we apply T5 to two novel tasks: closed-book question answering and fill-in-the-blank text generation with variable-sized blanks.

Closed-Book Question Answering
One way to use the text-to-text framework is on reading comprehension problems, where the model is fed some context along with a question and is trained to find the question's answer from the context. For example, one might feed the model the text from the Wikipedia article about Hurricane Connie along with the question "On what date did Hurricane Connie occur?" The model would then be trained to find the date "August 3rd, 1955" in the article. In fact, we achieved state-of-the-art results on the Stanford Question Answering Dataset (SQuAD) with this approach.

In our Colab demo and follow-up paper, we trained T5 to answer trivia questions in a more difficult "closed-book" setting, without access to any external knowledge. In other words, in order to answer a question T5 can only use knowledge stored in its parameters that it picked up during unsupervised pre-training. This can be considered a constrained form of open-domain question answering.
During pre-training, T5 learns to fill in dropped-out spans of text (denoted by <M>) from documents in C4. To apply T5 to closed-book question answer, we fine-tuned it to answer questions without inputting any additional information or context. This forces T5 to answer questions based on “knowledge” that it internalized during pre-training.
T5 is surprisingly good at this task. The full 11-billion parameter model produces the exact text of the answer 50.1%, 37.4%, and 34.5% of the time on TriviaQA, WebQuestions, and Natural Questions, respectively. To put these results in perspective, the T5 team went head-to-head with the model in a pub trivia challenge and lost! Try it yourself by clicking the animation below.
Fill-in-the-Blank Text Generation
Large language models like GPT-2 excel at generating very realistic looking-text since they are trained to predict what words come next after an input prompt. This has led to numerous creative applications like Talk To Transformer and the text-based game AI Dungeon. The pre-training objective used by T5 aligns more closely with a fill-in-the-blank task where the model predicts missing words within a corrupted piece of text. This objective is a generalization of the continuation task, since the “blanks” can appear at the end of the text as well.

To make use of this objective, we created a new downstream task called sized fill-in-the-blank, where the model is asked to replace a blank with a specified number of words. For example, if we give the model the input “I like to eat peanut butter and _4_ sandwiches,” we would train it to fill in the blank with approximately 4 words.

We fine-tuned T5 on this task using C4 and found the resulting outputs to be quite realistic. It’s especially fun to see how the model adjusts its predictions based on the requested size for the missing text. For example, with the input, “I love peanut butter and _N_ sandwiches,” the outputs looked like:

  • I love peanut butter and jelly sandwiches.
  • I love peanut butter and jelly on my sandwiches.
  • I love peanut butter and jelly, which is what makes good sandwiches.
  • I love peanut butter and jelly, Yum! You can’t beat peanut butter and jelly sandwiches.
  • I love peanut butter and bread. Thanks!! This looks delicious. I love all types of peanut butter, but especially peanut butter/jam sandwiches.
  • I love peanut butter and banana sandwiches and the Peanut Butter Chocolate Chip Cookie Bites are now very easy to prepare. The best part is that the PB&J Cookie Bites are the perfect size for kid lunch boxes and everyone will enjoy them. Kids love these sandwiches.
  • I love peanut butter and jelly! It's like an incredible childhood love now that I'm in my 30s. I'm obsessed with peanut butter and jelly sandwiches. I have made this recipe many times with great success. I'm guessing you could probably find that it's from a 1950s cookbook. I haven't seen it listed in a current one but it is in a 1930 version (they seem to have retired it). Hope you enjoy it ;) I hope you'll post what you do with your sandwiches.
  • I love peanut butter and banana sandwiches. Peanut butter and bananas were the original sandwich spread (also known as PB&J or Peanut Butter and Jelly), so they are probably my favorite. When I was a kid, I knew what peanut butter and bananas tasted like, but I didn't really think of them as one flavor. I did recognize PB & J's as just a sandwich spread, and not really two separate flavours. When I was in graduate school researching food allergies and intolerances, PB&B is one of the main themes that came up. PB&B was and still is the favorite sandwich spread on my school cafeteria tray, and it's still on my personal board in the kitchen. I've seen PB&B on more than one occasion over the years. I can always find one of the other types of peanut butter spread at the school cafeteria and I find it's the perfect spread for PB&B. You have several options for making your own peanut butter (I love this recipe from Cookie and Kate) or buying it at stores like Trader Joes, Publix, and Safeway. The flavor of PB&B varies, but I've found that white chocolate is my favorite to use. This simple sandwich is perfect for lunch or an afternoon snack. When you're on the go, you can use a store bought product, but it just wouldn't taste as good. These peanut butter n' banana sandwich spreads (as well as other peanut butter spreads) are great for on the go eating, but they can also be refrigerated for a few hours and then served at your next party. Preheat oven to 350 degrees. Using a kitchen timer, or using a microwave, heat butter in a saucepan and melt over low heat. Add vanilla extract and milk and heat until nearly boiling. Add peanut butter, bananas, sugar, salt, and pepper, mixing well to combine all ingredients. Spread peanut butter mixture on both sides of your bread. Top with banana slices and a slice of cheese. Toast the bread until both sides are golden brown. Remove from toast and sprinkle a little more sugar and peanut butter on the top. Enjoy while the bread is warm. Store in an airtight container up to one day. Assemble peanut butter and banana sandwich spread by spreading the peanut butter mixture on each slice of bread. Add a banana slice on top and then a PB & J sandwich. Enjoy while the bread is still warm. P.S. You might also like these peanut butter and jelly sandwiches.
Conclusion
We are excited to see how people use our findings, code, and pre-trained models to help jump-start their projects. Check out the Colab Notebook to get started, and share how you use it with us on Twitter!

Acknowledgements
This work has been a collaborative effort involving Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, Karishma Malkan, Noah Fiedel, and Monica Dinculescu.

Source: Google AI Blog


A New Workflow for Collaborative Machine Learning Research in Biodiversity



The promise of machine learning (ML) for species identification is coming to fruition, revealing its transformative potential in biodiversity research. International workshops such as FGVC and LifeCLEF feature competitions to develop top performing classification algorithms for everything from wildlife camera trap images to pressed flower specimens on herbarium sheets. The encouraging results that have emerged from these competitions inspired us to expand the availability of biodiversity datasets and ML models from workshop-scale to global-scale.

Bringing powerful ML algorithms to the communities that need them requires more than the traditional “big data + big compute” equation. Institutions ranging from natural history museums to citizen science groups take great care to collect and annotate datasets, and the data they share have enabled numerous scientific research publications. But central to the tradition of scholarly research are the conventions of citation and attribution, and it follows that as ML extends its reach into the life sciences, it should bring with it appropriate counterparts to those conventions. More broadly, there is a growing awareness of the importance of ethics, fairness, and transparency within the ML community. As institutions develop and deploy applications of ML at scale, it is critical that they be designed with these considerations in mind.

This week at Biodiversity Next, in collaboration with the Global Biodiversity Information Facility (GBIF), iNaturalist, and Visipedia, we are announcing a new workflow for biodiversity research institutions who would like to make use of ML. With its billion+ species occurrence count contributed by thousands of institutions around the globe, GBIF is playing a vital role in enabling this workflow, whether in terms of data aggregation, collaboration across teams, or standardizing citation practices. In the short term, the most important role relates to an emerging cultural shift in accepted practices for the use of mediated data for training of ML models. In the process of data mediation, GBIF helps ensure that training datasets for ML follow standardized licensing terms, use compatible taxonomies and data formats, and provide fair and sufficient data coverage for the ML task at hand by potentially sampling from multiple source datasets.

This new workflow comprises the following two components:
  1. To assist in developing and refining machine vision models, GBIF will package datasets, taking care to ensure license and citation practice are respected. The training datasets will be issued a Digital Object Identifier (DOI), and will be linked through the DOI citation graph.
  2. To assist application developers, Google and Visipedia will train and publish publicly accessible models with documentation on TensorFlow Hub. These models can then, in turn, be deployed in biodiversity research and citizen science efforts.
Case Study: Recognizing Fungi Species from Photos with the Interactive Mushroom Recognizer
As an illustration of the above workflow, we present an example of fungi recognition. The dataset in this case is curated by the Danish Mycological Society, and formatted, packaged, and shared by GBIF. The dataset provenance, model architecture, license information, and more can be found on the TF Hub model page, along with a live, interactive demonstration of the model that can run on user-supplied images.
Illustration of live, interactive Mushroom Recognizer, powered by a publicly available model trained on a fungi dataset provided by the Danish Mycological Society.
Invitation to Participate
For more information about this initiative, please visit the project page at GBIF. We look forward to engaging with institutions around the globe to enable new and innovative uses of ML for biodiversity.

Acknowledgements
We’d like to thank our collaborators at GBIF, iNaturalist, and Visipedia for working together to develop this workflow. At Google we would like to thank Christine Kaeser-Chen, Chenyang Zhang, Yulong Liu, Kiat Chuan Tan, Christy Cui, Arvi Gjoka, Denis Brulé, Cédric Deltheil, Clément Beauseigneur, Grace Chu, Andrew Howard, Sara Beery, and Katherine Chou.

Source: Google AI Blog


Project Ihmehimmeli: Temporal Coding in Spiking Neural Networks



The discoveries being made regularly in neuroscience are an ongoing source of inspiration for creating more efficient artificial neural networks that process information in the same way as biological organisms. These networks have recently achieved resounding success in domains ranging from playing board and video games to fine-grained understanding of video. However, there is one fundamental aspect of biological brains that artificial neural networks are not yet fully leveraging: temporal encoding of information. Preserving temporal information allows a better representation of dynamic features, such as sounds, and enables fast responses to events that may occur at any moment. Furthermore, despite the fact that biological systems can consist of billions of neurons, information can be carried by a single signal (‘spike’) fired by an individual neuron, with information encoded in the timing of the signal itself.

Based on this biological insight, project Ihmehimmeli explores how artificial spiking neural networks can exploit temporal dynamics using various architectures and learning settings. “Ihmehimmeli” is a Finnish tongue-in-cheek word for a complex tool or a machine element whose purpose is not immediately easy to grasp. The essence of this word captures our aim to build complex recurrent neural network architectures with temporal encoding of information. We use artificial spiking networks with a temporal coding scheme, in which more interesting or surprising information, such as louder sounds or brighter colours, causes earlier neuronal spikes. Along the information processing hierarchy, the winning neurons are those that spike first. Such an encoding can naturally implement a classification scheme where input features are encoded in the spike times of their corresponding input neurons, while the output class is encoded by the output neuron that spikes earliest.
The Ihmehimmeli project team holding a himmeli, a symbol for the aim to build recurrent neural network architectures with temporal encoding of information.
We recently published and open-sourced a model in which we demonstrated the computational capabilities of fully connected spiking networks that operate using temporal coding. Our model uses a biologically-inspired synaptic transfer function, where the electric potential on the membrane of a neuron rises and gradually decays over time in response to an incoming signal, until there is a spike. The strength of the associated change is controlled by the "weight" of the connection, which represents the synapse efficiency. Crucially, this formulation allows exact derivatives of postsynaptic spike times with respect to presynaptic spike times and weights. The process of training the network consists of adjusting the weights between neurons, which in turn leads to adjusted spike times across the network. Much like in conventional artificial neural networks, this was done using backpropagation. We used synchronization pulses, whose timing is also learned with backpropagation, to provide a temporal reference to the network.

We trained the network on classic machine learning benchmarks, with features encoded in time. The spiking network successfully learned to solve noisy Boolean logic problems and achieved a test accuracy of 97.96% on MNIST, a result comparable to conventional fully connected networks with the same architecture. However, unlike conventional networks, our spiking network uses an encoding that is in general more biologically-plausible, and, for a small trade-off in accuracy, can compute the result in a highly energy-efficient manner, as detailed below.

While training the spiking network on MNIST, we observed the neural network spontaneously shift between two operating regimes. Early during training, the network exhibited a slow and highly accurate regime, where almost all neurons fired before the network made a decision. Later in training, the network spontaneously shifted into a fast but slightly less accurate regime. This behaviour was intriguing, as we did not optimize for it explicitly. Thus spiking networks can, in a sense, be “deliberative”, or make a snap decision on the spot. This is reminiscent of the trade-off between speed and accuracy in human decision-making.
A slow (“deliberative”) network (top) and a fast (“impulsive”) network (bottom) classifying the same MNIST digit. The figures show a raster plot of spike times of individual neurons in individual layers, with synchronization pulses shown in orange. In this example, both networks classify the digit correctly; overall, the “slow” network achieves better accuracy than the “fast” network.
We were also able to recover representations of the digits learned by the spiking network by gradually adjusting a blank input image to maximize the response of a target output neuron. This indicates that the network learns human-like representations of the digits, as opposed to other possible combinations of pixels that might look “alien” to people. Having interpretable representations is important in order to understand what the network is truly learning and to prevent a small change in input from causing a large change in the result.
How the network “imagines” the digits 0, 1, 3 and 7.
This work is one example of an initial step that project Ihmehimmeli is taking in exploring the potential of time-based biology-inspired computing. In other on-going experiments, we are training spiking networks with temporal coding to control the walking of an artificial insect in a virtual environment, or taking inspiration from the development of the neural system to train a 2D spiking grid to predict words using axonal growth. Our goal is to increase our familiarity with the mechanisms that nature has evolved for natural intelligence, enabling the exploration of time-based artificial neural networks with varying internal states and state transitions.

Acknowledgements
The work described here was authored by Iulia Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo and Jyrki Alakuijala. We are grateful for all discussions and feedback on this work that we received from our colleagues at Google.

Source: Google AI Blog


Coral updates: Project tutorials, a downloadable compiler, and a new distributor

Posted by Vikram Tank (Product Manager), Coral Team

coral hardware

We’re committed to evolving Coral to make it even easier to build systems with on-device AI. Our team is constantly working on new product features, and content that helps ML practitioners, engineers, and prototypers create the next generation of hardware.

To improve our toolchain, we're making the Edge TPU Compiler available to users as a downloadable binary. The binary works on Debian-based Linux systems, allowing for better integration into custom workflows. Instructions on downloading and using the binary are on the Coral site.

We’re also adding a new section to the Coral site that showcases example projects you can build with your Coral board. For instance, Teachable Machine is a project that guides you through building a machine that can quickly learn to recognize new objects by re-training a vision classification model directly on your device. Minigo shows you how to create an implementation of AlphaGo Zero and run it on the Coral Dev Board or USB Accelerator.

Our distributor network is growing as well: Arrow will soon sell Coral products.

Announcing the 6th Fine-Grained Visual Categorization Workshop



In recent years, fine-grained visual recognition competitions (FGVCs), such as the iNaturalist species classification challenge and the iMaterialist product attribute recognition challenge, have spurred progress in the development of image classification models focused on detection of fine-grained visual details in both natural and man-made objects. Whereas traditional image classification competitions focus on distinguishing generic categories (e.g., car vs. butterfly), the FGVCs go beyond entry level categories to focus on subtle differences in object parts and attributes. For example, rather than pursuing methods that can distinguish categories, such as “bird”, we are interested in identifying subcategories such as “indigo bunting” or “lazuli bunting.”

Previous challenges attracted a large number of talented participants who developed innovative new models for image recognition, with more than 500 teams competing at FGVC5 at CVPR 2018. FGVC challenges have also inspired new methods such as domain-specific transfer learning and estimating test-time priors, which have helped fine-grained recognition tasks reach state-of-the-art performance on several benchmarking datasets.

In order to further spur progress in FGVC research, we are proud to sponsor and co-organize the 6th annual workshop on Fine-Grained Visual Categorization (FGVC6), to be held on June 17th in Long Beach, CA at CVPR 2019. This workshop brings together experts in computer vision with specialists focusing on biodiversity, botany, fashion, and the arts, to address the challenges of applying fine-grained visual categorization to real-life settings.

This Year’s Challenges
This year there will be a wide variety of competition topics, each highlighting unique challenges of fine-grained visual categorization, including an updated iNaturalist challenge, fashion & products, wildlife camera traps, food, butterflies & moths, fashion design, and cassava leaf disease. We are also delighted to introduce two new partnerships with world class institutions—The Metropolitan Museum of Art for the iMet Collection challenge and the New York Botanical Garden for the Herbarium challenge.
The FGVC workshop at CVPR focuses on subordinate categories, including (from left to right, top to bottom) animal species from wildlife camera traps, retail products, fashion attributes, cassava leaf disease, Melastomataceae species from herbarium sheets, animal species from citizen science photos, butterfly and moth species, cuisine of dishes, and fine-grained attributes for museum art objects.
In the iMet Collection challenge, participants compete to train models on artistic attributes including object presence, culture, content, theme, and geographic origin. The Metropolitan Museum of Art provided a large training dataset for this task based on subject matter experts’ descriptions of their museum collections. This dataset highlights the challenge of inferring fine-grained attributes that are grounded in the visual context indirectly (e.g., period, culture, medium).
A diverse sample of images included in the iMet Collection challenge dataset. Images were taken from the Metropolitan Museum of Art’s public domain dataset.
The iMet Collection challenge is also noteworthy for its status as the first image-based Kernels-only competition, a recently introduced option on Kaggle that levels the playing field for data scientists who might not otherwise have access to adequate computational resources. Kernel competitions provide all participants with the same hardware allowances, giving rise to a more balanced competition. Moreover, the winning models tend to be simpler than their counterparts in other competitions, since the participants must work within the compute constraints imposed by the Kernels platform. At the time of writing, the iMet Collection challenge has over 250 participating teams.

In the Herbarium challenge, researchers are invited to tackle the problem of classifying species from the flowering plant family Melastomataceae. This challenge is distinguished from the iNaturalist competition, since the included images depict dried specimens preserved on herbarium sheets, exclusively. Herbarium sheets are essential to plant science, as they not only preserve the key details of the plants for identification and DNA analysis, but also provide a rare perspective into plant ecology in a historical context. As the world’s second largest herbarium, NYBG’s Steere Herbarium collection contributed a dataset of over 46,000 specimens for this year’s challenge.
In the Herbarium challenge, participants will identify species from the flowering plant family Melastomataceae. The New York Botanical Garden (NYBG) provided a dataset of over 46,000 herbarium specimens including over 680 species. Images used with permission of the NYBG.
Every one of this year’s challenges requires deep engagement with subject matter experts, in addition to institutional coordination. By teeing up image recognition challenges in a standard format, the FGVC workshop paves the way for technology transfer from the top of the Kaggle leaderboards into the hands of everyday users via mobile apps such as Seek by iNaturalist and Merlin Bird ID. We anticipate the techniques developed by our competition participants will not only push the frontier of fine-grained recognition, but also be beneficial for applying machine vision to advance scientific exploration and curatorial studies.

Invitation to Participate
We invite teams to participate in these competitions to help advance the state-of-the-art in fine-grained image recognition. Deadlines for entry into the competitions range from May 26 to June 3, depending on the challenge. The results of these competitions will be presented at the FGVC6 workshop at CVPR 2019, and will provide broad exposure to the top performing teams. We are excited to encourage the community's development of more accurate and broadly impactful algorithms in the field of fine-grained visual categorization!

Acknowledgements
We’d like to thank our colleagues and friends on the FGVC6 organizing committee for working together to advance this important area. At Google we would like to thank Hartwig Adam, Chenyang Zhang, Yulong Liu, Kiat Chuan Tan, Mikhail Sirotenko, Denis Brulé, Cédric Deltheil, Timnit Gebru, Ernest Mwebaze, Weijun Wang, Grace Chu, Jack Sim, Andrew Howard, R.V. Guha, Srikanth Belwadi, Tanya Birch, Katherine Chou, Maggie Demkin, Elizabeth Park, and Will Cukierski.

Source: Google AI Blog


Introducing Coral: Our platform for development with local AI

Posted by Billy Rutledge (Director) and Vikram Tank (Product Mgr), Coral Team

AI can be beneficial for everyone, especially when we all explore, learn, and build together. To that end, Google's been developing tools like TensorFlow and AutoML to ensure that everyone has access to build with AI. Today, we're expanding the ways that people can build out their ideas and products by introducing Coral into public beta.

Coral is a platform for building intelligent devices with local AI.

Coral offers a complete local AI toolkit that makes it easy to grow your ideas from prototype to production. It includes hardware components, software tools, and content that help you create, train and run neural networks (NNs) locally, on your device. Because we focus on accelerating NN's locally, our products offer speedy neural network performance and increased privacy — all in power-efficient packages. To help you bring your ideas to market, Coral components are designed for fast prototyping and easy scaling to production lines.

Our first hardware components feature the new Edge TPU, a small ASIC designed by Google that provides high-performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at 100+ fps, in a power efficient manner.

Coral Camera Module, Dev Board and USB Accelerator

For new product development, the Coral Dev Board is a fully integrated system designed as a system on module (SoM) attached to a carrier board. The SoM brings the powerful NXP iMX8M SoC together with our Edge TPU coprocessor (as well as Wi-Fi, Bluetooth, RAM, and eMMC memory). To make prototyping computer vision applications easier, we also offer a Camera that connects to the Dev Board over a MIPI interface.

To add the Edge TPU to an existing design, the Coral USB Accelerator allows for easy integration into any Linux system (including Raspberry Pi boards) over USB 2.0 and 3.0. PCIe versions are coming soon, and will snap into M.2 or mini-PCIe expansion slots.

When you're ready to scale to production we offer the SOM from the Dev Board and PCIe versions of the Accelerator for volume purchase. To further support your integrations, we'll be releasing the baseboard schematics for those who want to build custom carrier boards.

Our software tools are based around TensorFlow and TensorFlow Lite. TF Lite models must be quantized and then compiled with our toolchain to run directly on the Edge TPU. To help get you started, we're sharing over a dozen pre-trained, pre-compiled models that work with Coral boards out of the box, as well as software tools to let you re-train them.

For those building connected devices with Coral, our products can be used with Google Cloud IoT. Google Cloud IoT combines cloud services with an on-device software stack to allow for managed edge computing with machine learning capabilities.

Coral products are available today, along with product documentation, datasheets and sample code at g.co/coral. We hope you try our products during this public beta, and look forward to sharing more with you at our official launch.