A Benchmark and Empirical Analysis for Replay Strategies in Continual Learning

• URL: http://arxiv.org/abs/2208.02660v1
• Date: Thu, 4 Aug 2022 13:48:11 GMT
• Title: A Benchmark and Empirical Analysis for Replay Strategies in Continual Learning
• Authors: Qihan Yang, Fan Feng, Rosa Chan
• Abstract summary: Computational systems are not, in general, capable of learning tasks sequentially. This paper makes an in-depth evaluation of the memory replay methods. All experiments are conducted on multiple datasets under various domains.
• Score: 2.922007656878633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the capacity of continual learning, humans can continuously acquire knowledge throughout their lifespan. However, computational systems are not, in general, capable of learning tasks sequentially. This long-standing challenge for deep neural networks (DNNs) is called catastrophic forgetting. Multiple solutions have been proposed to overcome this limitation. This paper makes an in-depth evaluation of the memory replay methods, exploring the efficiency, performance, and scalability of various sampling strategies when selecting replay data. All experiments are conducted on multiple datasets under various domains. Finally, a practical solution for selecting replay methods for various data distributions is provided.

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