Poster

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

Sayna Ebrahimi · Suzanne Petryk · Akash Gokul · William Gan · Joseph E Gonzalez · Marcus Rohrbach · trevor darrell

Virtual

Keywords: [ XAI ] [ lifelong learning ] [ catastrophic forgetting ] [ continual learning ] [ explainability ]

[ Abstract ]
[ Slides [ Paper ]
Mon 3 May 5 p.m. PDT — 7 p.m. PDT

Abstract:

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the \textit{evidence} for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has ``the right reasons'' for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}.

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