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In class-incremental learning, the objective is to learn a number of classessequentially without having access to the whole training data. However, due toa problem known as catastrophic forgetting, neural networks suffer substantialperformance drop in such settings. The problem is often approached byexperience replay, a method which stores a limited number of samples to bereplayed in future steps to reduce forgetting of the learned classes. Whenusing a pretrained network as a feature extractor, we show that instead oftraining a single classifier incrementally, it is better to train a number ofspecialized classifiers which do not interfere with each other yet cancooperatively predict a single class. Our experiments on CIFAR100 dataset showthat the proposed method improves the performance over SOTA by a large margin.

Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers