Active Learning for Continual Learning: Keeping the Past Alive in the Present
Keywords: active learning, continual learning, Fisher information
TL;DR: Propose a novel active continual learning method based on the accumulative informativeness to avoid catastrophic forgetting
Abstract: *Continual learning (CL)* enables deep neural networks to adapt to ever-changing data distributions. In practice, there may be scenarios where annotation is costly, leading to *active continual learning (ACL)*, which performs *active learning (AL)* for the CL scenarios when reducing the labeling cost by selecting the most informative subset is preferable. However, conventional AL strategies are not suitable for ACL, as they focus solely on learning the new knowledge, leading to *catastrophic forgetting* of previously learned tasks. Therefore, ACL requires a new AL strategy that can balance the prevention of catastrophic forgetting and the ability to quickly learn new tasks. In this paper, we propose **AccuACL**, **Accu**mulated informativeness-based **A**ctive **C**ontinual **L**earning, by achieving an optimal balance between the two required capabilities of ACL, as well as alleviating the scalability issue of Fisher information-based AL. Extensive experiments demonstrate that AccuACL significantly outperforms AL baselines across various CL algorithms, increasing the average accuracy and forgetting by 23.8% and 17.0%, respectively, in average.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 1747
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