Class Incremental Learning via Semantic Information Mapping and Background Information Calibrating

Replaying episodic memory hippocampus-based is a promising class incremental learning (CIL) method, and it must address the problem of catastrophic forgetting. However, most current studies have ignored background information provided by the parahippocampal gyrus. Therefore, in this paper, the topological data analysis (TDA) technology is proposed for the first time to simulate the biological function of the parahippocampal gyrus in order to obtain multi-scale background information such as geometric and topological. Based on the idea, a novel CIL method via semantic information mapping and background information calibrating (SiBiCIL) is proposed. It takes categorical prototypes as semantic information and uses the constructed mapping function to generate the prototype classifiers. The classification results are then calibrated using background information extracted from the point-clouded data. In addition to the cross-entropy loss and the knowledge distillation loss, a difference loss is defined to maintain the distinctiveness between the prototype classifiers. These losses collectively contribute to updating the model and retaining previous classes’ knowledge, as well as a way to mitigate catastrophic forgetting. Compared to other methods with the total number of exemplars no more than 500, the experimental results on CIFAR-10, CIFAR-100, ImageNet-100, and Protein_family datasets show that highest percentage increases of average classification accuracies are 49.72%, 7.15%, 1.96%, and 12.35% respectively, while the forgetting rate has concurrently decreased by 32.35%, 9.80%, 12.50%, and 5.41%. Moreover, SiBiCIL significantly reduces the episodic memory buffer budget by an average of 76.39%.