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Existing continual learning methods use Batch Normalization (BN) tofacilitate training and improve generalization across tasks. However, thenon-i.i.d and non-stationary nature of continual learning data, especially inthe online setting, amplify the discrepancy between training and testing in BNand hinder the performance of older tasks. In this work, we study thecross-task normalization effect of BN in online continual learning where BNnormalizes the testing data using moments biased towards the current task,resulting in higher catastrophic forgetting. This limitation motivates us topropose a simple yet effective method that we call Continual Normalization (CN)to facilitate training similar to BN while mitigating its negative effect.Extensive experiments on different continual learning algorithms and onlinescenarios show that CN is a direct replacement for BN and can providesubstantial performance improvements. Our implementation is available at\url{https://github.com/phquang/Continual-Normalization}.

Continual Normalization: Rethinking Batch Normalization for Online Continual Learning