has revolutionized computer vision
, with state-of-the-art deep networks learning useful representations
directly from raw pixels, leading to unprecedented performance on many vision tasks. However, learning these representations from scratch typically requires hundreds of thousands of training examples. This burden can be reduced by using pre-trained representations, which have become widely available through services such as TensorFlow Hub
(TF Hub) and PyTorch Hub
. But their ubiquity can itself be a hindrance. For example, for the task of extracting features from images, there can be over 100 models from which to choose. It is hard to know which methods provide the best representations, since different sub-fields use different evaluation protocols, which do not always reflect the final performance on new tasks.
The overarching goal of representation research is to learn representations a single time on large amounts of generic data without the need to train them from scratch for each task, thus reducing data requirements across all vision tasks. But in order to reach that goal, the research community must have a uniform benchmark against which existing and future methods can be evaluated.
To address this problem, we are releasing "The Visual Task Adaptation Benchmark
" (VTAB, available on GitHub
), a diverse, realistic, and challenging representation benchmark based on one principle — a better representation is one that yields better performance on unseen tasks, with limited in-domain data. Inspired by benchmarks that have driven progress in other fields of machine learning (ML), such as ImageNet
for natural image classification, GLUE
for Natural Language Processing, and Atari
for reinforcement learning, VTAB follows similar guidelines: (i) minimal constraints on solutions to encourage creativity; (ii) a focus on practical considerations; and (iii) challenging tasks for evaluation.The Benchmark
VTAB is an evaluation protocol designed to measure progress towards general and useful visual representations, and consists of a suite of evaluation vision tasks that a learning algorithm must solve. These algorithms may use pre-trained visual representations to assist them and must satisfy only two requirements:
i) They must not be pre-trained on any of the data (labels or input images) used in the downstream evaluation tasks.
ii) They must not contain hardcoded, task-specific, logic. Alternatively put, the evaluation tasks must be treated like a test set — unseen.
These constraints ensure that solutions that are successful when applied to VTAB will be able to generalize to future tasks.
The VTAB protocol begins with the application of an algorithm (A
) to a number of independent tasks, drawn from a broad distribution of vision problems. The algorithm may be pre-trained on upstream data to yield a model that contains visual representations, but it must also define an adaptation strategy that consumes a small training set for each downstream task and return a model that makes task-specific predictions. The algorithm’s final score is its average test score across tasks.
|The VTAB protocol. Algorithm A is applied to many tasks T, drawn from a broad distribution of vision problems PT. In the example, pet classification, remote sensing, and maze localization are shown.|
VTAB includes 19 evaluation tasks that span a variety of domains, divided into three groups — natural
, and structured
image tasks include images of the natural world captured through standard cameras, representing generic objects, fine-grained classes, or abstract concepts. Specialized
tasks utilize images captured using specialist equipment, such as medical images
or remote sensing
. The structured
tasks often derive from artificial environments that target understanding of specific changes between images, such as predicting the distance to an object in a 3D scene (e.g., DeepMind Lab
), counting objects (e.g., CLEVR
), or detecting orientation (e.g., dSprites
for disentangled representations
While highly diverse, all of the tasks in VTAB share one common feature — people can solve them relatively easily after training on just a few examples. To assess algorithmic generalization to new tasks with limited data, performance is evaluated using only 1000 examples per task. Evaluation using the full dataset can be performed for comparison with previous publications.Findings Using VTAB
We performed a large scale study testing a number of popular visual representation learning algorithms against VTAB. The study included generative models (GANs
), self-supervised models, semi-supervised models and supervised models. All of the algorithms were pre-trained on the ImageNet
dataset. We also compared each of these approaches using no pre-trained representations, i.e., training “from-scratch”. The figure below summarizes the main pattern of results.
|Performance of different classes of representation learning algorithms across different task groups: natural, specialized and structured. Each bar shows the average performance of all methods in that class across all tasks in the group.|
Overall we find that generative models do not perform as well as the other methods, even worse than from-scratch training. However, self-supervised models perform much better, significantly outperforming from-scratch training. Better still is supervised learning using the ImageNet labels. Interestingly, while supervised learning is significantly better on the Natural
group of tasks, self-supervised learning is close on the other two groups whose domains are more dissimilar to ImageNet.
The best performing representation learning algorithm, of those we tested, is S4L
, which combines both supervised and self-supervised pre-training losses. The figure below contrasts S4L with standard supervised ImageNet pre-training. S4L appears to improve performance particularly on the Structured
tasks. However, representation learning yields a much smaller benefit over training from-scratch groups other than the Natural
tasks, indicating that there is much progress required to attain a universal visual representation.
|Top: Performance of S4L versus from-scratch training. Each bar corresponds to a task. Positive-valued bars indicate tasks where S4L outperforms from-scratch. Negative bars indicate that from-scratch performed better. Bottom: S4L versus Supervised training on ImageNet. Positive bars indicate that S4L performs better. The bar colour indicates the task group: Red=Natural, Green=Specialized, Blue=Structured. We can see that additional self-supervision tends to help on structured tasks beyond just using ImageNet labels.|
The code to run VTAB is available on GitHub
, including the 19 evaluation datasets and exact data splits. Having a publicly available set of benchmarks ensures the reproducibility of results. Progress is tracked with the public leaderboard
, and the models evaluated are uploaded to TF Hub
for public use and reproduction. A shell script is provided to perform adaptation and evaluation on all the tasks, with a standardized evaluation protocol making VTAB readily accessible across the industry. Since VTAB can be executed on both TPU
, it is highly efficient. One can obtain comparable results with a single NVIDIA Tesla P100 accelerator in a few hours.
The Visual Task Adaptation Benchmark has helped us better understand which visual representations generalize to the broad spectrum of vision tasks, and provides direction for future research. We hope these resources are useful in driving progress toward general and practical visual representations, and as a result, affords deep learning to the long tail of vision problems with limited labelled data.AcknowledgementsThe core team behind this work includes Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, and Sylvain Gelly.