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

• URL: http://arxiv.org/abs/2104.01577v2
• Date: Tue, 6 Apr 2021 05:47:21 GMT
• Title: Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers
• Authors: Sobirdzhon Bobiev
• Abstract summary: In class-incremental learning, the objective is to learn a number of classes sequentially without having access to the whole training data. Our experiments on CIFAR100 dataset show that the proposed method improves the performance over SOTA by a large margin.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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