Discriminative Representation Loss (DRL): A More Efficient Approach Than Gradient Re-projection in continual learning

Yu Chen, Tom Diethe, Peter Flach

Research output: Contribution to journalArticle (Academic Journal)

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Abstract

The use of episodic memories in continual learning has been shown to be effective in terms of alleviating catastrophic forgetting. In recent studies, several gradient-based approaches have been developed to make more efficient use of compact episodic memories, which constrain the gradients resulting from new samples with those from memorized samples, aiming to reduce the diversity of gradients from different tasks. In this paper, we reveal the relation between diversity of gradients and discriminativeness of representations, demonstrating connections between Deep Metric Learning and continual learning. Based on these findings,we propose a simple yet highly efficient method - Discriminative Representation Loss (DRL) - for continual learning. In comparison with several state-of-the-art methods, DRL shows effectiveness with low computational cost on multiple benchmark experiments in the setting of online continual learning.
Original languageEnglish
Number of pages15
JournalarXiv
Publication statusUnpublished - 19 Jun 2020

Keywords

  • stat.ML
  • cs.LG

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