Information Retrieval Optimization for Non-Exemplar Class Incremental Learning
Shuai Guo, Yang Gu, Yuan Ma, Yingwei Zhang, Weining Weng, Jun Liu, Weiwei Dai, Yiqiang Chen · 2024
Existing non-example class-incremental learning (NECIL) methods usually utilize a combination strategy of replay mechanism and knowledge distillation. However, this combination strategy only focuses on the preservation of old information quantitatively, ignoring the preservation quality. When the old knowledge has wrong redundant information, catastrophic forgetting is more likely to occur. Therefore, obtaining adequate information without impurities as much as possible and removing invalid or even harmful information has become an effective solution to improve the performance of NECIL. This process is consistent with the information bottleneck (IB) theory. Thus, we propose a new NECIL method based on the IB framework. By using the different information obtained from the new and old class samples and the implicit knowledge in the teacher model training process, the error of harmful redundant information learned is eliminated. Specifically, we propose two optimization strategies that align with the two optimization processes of the information bottleneck. Firstly, we employ a pseudo-prototype selection mechanism that selectively incorporates pseudo-samples into the learning process of new and old categories, thus enhancing the distinction between new and old categories and diminishing the mutual information between the input and intermediate features. Secondly, we introduce an attention-based feature distillation method that regulates the distillation strength between feature pairs based on their similarity, thereby augmenting the mutual information between intermediate features and output prediction. Extensive experiments on three benchmarks demonstrate that the proposed method exhibits significant incremental performance improvements over existing methods.