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arXiv · 2207.05897

D-CBRS: Accounting For Intra-Class Diversity in Continual Learning

Yasin Findik·Farhad Pourkamali-Anaraki
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Abstract

Continual learning -- accumulating knowledge from a sequence of learning experiences -- is an important yet challenging problem. In this paradigm, the model's performance for previously encountered instances may substantially drop as additional data are seen. When dealing with class-imbalanced data, forgetting is further exacerbated. Prior work has proposed replay-based approaches which aim at reducing forgetting by intelligently storing instances for future replay. Although Class-Balancing Reservoir Sampling (CBRS) has been successful in dealing with imbalanced data, the intra-class diversity has not been accounted for, implicitly assuming that each instance of a class is equally informative. We present Diverse-CBRS (D-CBRS), an algorithm that allows us to consider within class diversity when storing instances in the memory. Our results show that D-CBRS outperforms state-of-the-art memory management continual learning algorithms on data sets with considerable intra-class diversity.

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Yasin Findik, Farhad Pourkamali-Anaraki. 2022-07-13. D-CBRS: Accounting For Intra-Class Diversity in Continual Learning. https://arxiv.org/abs/2207.05897

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