Avoid Being a Shortcut Learner through Library-Based Re-Learning
Keywords: Continual learning, Data-Efficient Learning, Information Theory
Abstract: Replay-based methods provide a promising solution to address catastrophic forgetting issue in continual learning. They try to retain previous knowledge by using a small amount of data from previous tasks stored in a fix-sized buffer. In this work, we invoke the information bottleneck principles and reveal some fundamental limitations of those methods on their effectiveness in capturing the truly important features from the prior tasks by relying on the buffer data selected according to the model's performance on known tasks. Since future tasks are not accessible during model training and buffer construction, the trained model and the buffer data tend to be biased towards making accurate predictions on the labels of known tasks. However, when new task samples are introduced along with labels, the biased model and the buffer data become less effective in differentiating samples of the old tasks from those of the new ones. Inspired by the way humans learn over time, we propose a novel relearning technique that makes use of additional past data, referred to as the library, to test how much information the model loses after learning the new task. We then realign the model towards those forgotten samples by training on a carefully selected small subset samples from the library for a few epochs with comparable computational cost as existing replay-based models. The experimental results on multiple real-world datasets demonstrate that the proposed relearning process can improve the performance of the state-of-the-art continual learning methods by a large margin.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 3923
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