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arXiv · 2104.01577

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

Sobirdzhon Bobiev·Adil Khan·Syed Muhammad Ahsan Raza Kazmi
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Abstract

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

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Sobirdzhon Bobiev, Adil Khan, Syed Muhammad Ahsan Raza Kazmi. 2021-04-04. Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers. https://arxiv.org/abs/2104.01577

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