Incremental sequential three-way decision based on continual learning network

Abstract

Continual learning has attracted much attention in recent years, and many continual learning methods based on deep neural networks have been proposed. However, several important problems about these methods may lead to high decision cost and affect the practical application of continual learning networks. First, continual learning networks treat all categories equally, although the unbalance of misclassification cost happens in real-world cases. Second, there is a trade-off between learning new knowledge and keep old knowledge, which leads to the forgetting of old knowledge (i.e., the catastrophic forgetting). Third, even if low confidence of a sample, the continual learning methods based on the neural network will still give a clear classification result. We propose a sequential three-way decision model for continual learning to address these problems, named Incremental Sequential Three-Way Decision model (ISTWD). Introducing cost-sensitive sequential three-way decision to continual learning network, ISTWD reduces the decision cost of continual learning, which may alleviate the potentially high cost caused by the accuracy loss in continual learning. Besides, ISTWD includes a checkpoint procedure to judge whether the process of continual learning should stop. Experimental results on CIFAR-100 and Tiny-ImageNet verify the effectiveness of our method.