Batch Normalization (BN) is an essential layer for training neural network models in various computer vision tasks. It has been widely used in continual learning scenarios with little discussion, but we find that BN should be carefully applied, particularly for the exemplar memory based class incremental learning (CIL). We first analyze that the empirical mean and variance obtained for normalization in a BN layer become highly biased toward the current task. To tackle its significant problems in training and test phases, we propose Task-Balanced Batch Normalization (TBBN). Given each mini-batch imbalanced between the current and previous tasks, TBBN first reshapes and repeats the batch, calculating near task-balanced mean and variance. Second, we show that when the affine transformation parameters of BN are learned from a reshaped feature map, they become less-biased toward the current task. Based on our extensive CIL experiments with CIFAR-100 and ImageNet-100 datasets, we demonstrate that our TBBN is easily applicable to most of existing exemplar-based CIL algorithms, improving their performance by decreasing the forgetting on the previous tasks.

本研究通过针对基于样本的类增量学习（CIL）任务开发了一种新的Batch Normalization（BN）更新模式——Task-Balanced BN（TBBN），通过使用reshape和repeat操作以及水平拼接任务平衡批来更好地解决训练数据不平衡问题，实验结果表明TBBN是一种超参数自由的变体，并且在CIFAR-100，ImageNet-100和五个不同的任务数据集上都优于其他的BN变体。

基于范例的类增量学习的归一化平衡化