Formalizing the Generalization-Forgetting Trade-off in Continual LearningDownload PDF

21 May 2021, 20:45 (edited 14 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Continual learning, dynamic programming, game theory, differential equations
  • TL;DR: We advance state of the art by introducing balanced continual learning to model the generalization-forgetting tradeoff as a two player sequential game.
  • Abstract: We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game. In this approach, player 1 maximizes the cost due to lack of generalization whereas player 2 minimizes the cost due to catastrophic forgetting. We show theoretically that a balance point between the two players exists for each task and that this point is stable (once the balance is achieved, the two players stay at the balance point). Next, we introduce balanced continual learning (BCL), which is designed to attain balance between generalization and forgetting and empirically demonstrate that BCL is comparable to or better than the state of the art.
  • Supplementary Material: pdf
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  • Code: https://github.com/krm9c/Balanced-Continual-Learning.git
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