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Continual learning -- accumulating knowledge from a sequence of learningexperiences -- is an important yet challenging problem. In this paradigm, themodel's performance for previously encountered instances may substantially dropas additional data are seen. When dealing with class-imbalanced data,forgetting is further exacerbated. Prior work has proposed replay-basedapproaches which aim at reducing forgetting by intelligently storing instancesfor future replay. Although Class-Balancing Reservoir Sampling (CBRS) has beensuccessful in dealing with imbalanced data, the intra-class diversity has notbeen accounted for, implicitly assuming that each instance of a class isequally informative. We present Diverse-CBRS (D-CBRS), an algorithm that allowsus to consider within class diversity when storing instances in the memory. Ourresults show that D-CBRS outperforms state-of-the-art memory managementcontinual learning algorithms on data sets with considerable intra-classdiversity.

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