"Continual normalization: Rethinking batch normalization for online con" by Quang PHAM, Chenghao LIU et al.
 

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2024

Abstract

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose 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 online scenarios show that CN is a direct replacement for BN and can provide substantial performance improvements. Our implementation is available at https://github.com/phquang/Continual-Normalization.

Keywords

Continual learning, Generalisation, Learning data, Learning methods, Nonstationary, Normalisation, Normalization effect, On-line setting, Performance, Training and testing

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management

Publication

Proceedings of the 10th International Conference on Learning Representations, ICLR 2022, April 25-29

First Page

1

Last Page

20

Publisher

ICLR

City or Country

Wisconsin, USA Wisconsin, USA

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