Supervised learning is a common approach to machine learning (ML) in which the model is trained using data that is labeled appropriately for the task at hand. Ordinary supervised learning trains on independent and identically distributed (IID) data, where all training examples are sampled from a fixed set of classes, and the model has access to these examples throughout the entire training phase. In contrast, continual learning tackles the problem of training a single model on changing data distributions where different classification tasks are presented sequentially. This is particularly important, for example, to enable autonomous agents to process and interpret continuous streams of information in real-world scenarios.
To illustrate the difference between supervised and continual learning, consider two tasks: (1) classify cats vs. dogs and (2) classify pandas vs. koalas. In supervised learning, which uses IID, the model is given training data from both tasks and treats it as a single 4-class classification problem. However, in continual learning, these two tasks arrive sequentially, and the model only has access to the training data of the current task. As a result, such models tend to suffer from performance degradation on the previous tasks, a phenomenon called catastrophic forgetting.
Mainstream solutions try to address catastrophic forgetting by buffering past data in a “rehearsal buffer” and mixing it with current data to train the model. However, the performance of these solutions depends heavily on the size of the buffer and, in some cases, may not be possible at all due to data privacy concerns. Another branch of work designs task-specific components to avoid interference between tasks. But these methods often assume that the task at test time is known, which is not always true, and they require a large number of parameters. The limitations of these approaches raise critical questions for continual learning: (1) Is it possible to have a more effective and compact memory system that goes beyond buffering past data? (2) Can one automatically select relevant knowledge components for an arbitrary sample without knowing its task identity?
In “Learning to Prompt for Continual Learning”, presented at CVPR2022, we attempt to answer these questions. Drawing inspiration from prompting techniques in natural language processing, we propose a novel continual learning framework called Learning to Prompt (L2P). Instead of continually re-learning all the model weights for each sequential task, we instead provide learnable task-relevant “instructions'' (i.e., prompts) to guide pre-trained backbone models through sequential training via a pool of learnable prompt parameters. L2P is applicable to various challenging continual learning settings and outperforms previous state-of-the-art methods consistently on all benchmarks. It achieves competitive results against rehearsal-based methods while also being more memory efficient. Most importantly, L2P is the first to introduce the idea of prompting in the field of continual learning.
Prompt Pool and Instance-Wise Query
Given a pre-trained Transformer model, “prompt-based learning” modifies the original input using a fixed template. Imagine a sentiment analysis task is given the input “I like this cat”. A prompt-based method will transform the input to “I like this cat. It looks X”, where the “X” is an empty slot to be predicted (e.g., “nice”, “cute”, etc.) and “It looks X” is the so-called prompt. By adding prompts to the input, one can condition the pre-trained models to solve many downstream tasks. While designing fixed prompts requires prior knowledge along with trial and error, prompt tuning prepends a set of learnable prompts to the input embedding to instruct the pre-trained backbone to learn a single downstream task, under the transfer learning setting.
In the continual learning scenario, L2P maintains a learnable prompt pool, where prompts can be flexibly grouped as subsets to work jointly. Specifically, each prompt is associated with a key that is learned by reducing the cosine similarity loss between matched input query features. These keys are then utilized by a query function to dynamically look up a subset of task-relevant prompts based on the input features. At test time, inputs are mapped by the query function to the top-N closest keys in the prompt pool, and the associated prompt embeddings are then fed to the rest of the model to generate the output prediction. At training, we optimize the prompt pool and the classification head via the cross-entropy loss.
Intuitively, similar input examples tend to choose similar sets of prompts and vice versa. Thus, prompts that are frequently shared encode more generic knowledge while other prompts encode more task-specific knowledge. Moreover, prompts store high-level instructions and keep lower-level pre-trained representations frozen, thus catastrophic forgetting is mitigated even without the necessity of a rehearsal buffer. The instance-wise query mechanism removes the necessity of knowing the task identity or boundaries, enabling this approach to address the under-investigated challenge of task-agnostic continual learning.
Effectiveness of L2P
We evaluate the effectiveness of L2P in different baseline methods using an ImageNet pre-trained Vision Transformer (ViT) on representative benchmarks. The naïve baseline, called Sequential in the graphs below, refers to training a single model sequentially on all tasks. The EWC model adds a regularization term to mitigate forgetting and the Rehearsal model saves past examples to a buffer for mixed training with current data. To measure the overall continual learning performance, we measure both the accuracy and the average difference between the best accuracy achieved during training and the final accuracy for all tasks (except the last task), which we call forgetting. We find that L2P outperforms the Sequential and EWC methods significantly in both metrics. Notably, L2P even surpasses the Rehearsal approach, which uses an additional buffer to save past data. Because the L2P approach is orthogonal to Rehearsal, its performance could be further improved if it, too, used a rehearsal buffer.
We also visualize the prompt selection result from our instance-wise query strategy on two different benchmarks, where one has similar tasks and the other has varied tasks. The results indicate that L2P promotes more knowledge sharing between similar tasks by having more shared prompts, and less knowledge sharing between varied tasks by having more task-specific prompts.
In this work, we present L2P to address key challenges in continual learning from a new perspective. L2P does not require a rehearsal buffer or known task identity at test time to achieve high performance. Further, it can handle various complex continual learning scenarios, including the challenging task-agnostic setting. Because large-scale pre-trained models are widely used in the machine learning community for their robust performance on real-world problems, we believe that L2P opens a new learning paradigm towards practical continual learning applications.
We gratefully acknowledge the contributions of other co-authors, including Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister. We would also like to thank Chun-Liang Li, Jeremy Martin Kubica, Sayna Ebrahimi, Stratis Ioannidis, Nan Hua, and Emmanouil Koukoumidis, for their valuable discussions and feedback, and Tom Small for figure creation.