Publication

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

International Conference on Learning Representations (ICLR)


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 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 https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons.

Related Publications

All Publications

SIGGRAPH - August 9, 2021

Deep Relightable Appearance Models for Animatable Faces

Sai Bi, Stephen Lombardi, Shunsuke Saito, Tomas Simon, Shih-En Wei, Kevyn Mcphail, Ravi Ramamoorthi, Yaser Sheikh, Jason Saragih

AISTATS - April 13, 2021

Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors

Nikhil Mehta, Kevin J Liang, Vinay K Verma, Lawrence Carin

NeurIPS - December 6, 2020

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

NeurIPS - December 7, 2020

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy