Related papers: Using Hindsight to Anchor Past Knowledge in Continual Learning

Using Hindsight to Anchor Past Knowledge in Continual Learning

URL: http://arxiv.org/abs/2002.08165v2
Date: Tue, 2 Mar 2021 08:05:50 GMT
Title: Using Hindsight to Anchor Past Knowledge in Continual Learning
Authors: Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip Torr, David Lopez-Paz
Abstract summary: In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. In this work, we call anchoring, where the learner uses bilevel optimization to update its knowledge on the current task, while keeping intact the predictions on past tasks.
Score: 36.271906785418864
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. To address such catastrophic forgetting, many continual learning methods implement different types of experience replay, re-learning on past data stored in a small buffer known as episodic memory. In this work, we complement experience replay with a new objective that we call anchoring, where the learner uses bilevel optimization to update its knowledge on the current task, while keeping intact the predictions on some anchor points of past tasks. These anchor points are learned using gradient-based optimization to maximize forgetting, which is approximated by fine-tuning the currently trained model on the episodic memory of past tasks. Experiments on several supervised learning benchmarks for continual learning demonstrate that our approach improves the standard experience replay in terms of both accuracy and forgetting metrics and for various sizes of episodic memories.

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