Structural Knowledge Organization and Transfer for Class-Incremental Learning

Yu Liu, Xiaopeng Hong, Xiaoyu Tao, Songlin Dong, Jingang Shi, Yihong Gong

Abstract: Deep models are vulnerable to catastrophic forgetting when fine-tuned on new data. Popular distillation-based methods usually neglect the relations between data samples and may eventually forget essential structural knowledge. To solve these shortcomings, we propose a structural graph knowledge distillation based incremental learning framework to preserve both the positions of samples and their relations. Firstly, a memory knowledge graph (MKG) is generated to fully characterize the structural knowledge of historical tasks. Secondly, we develop a graph interpolation mechanism to enrich the domain of knowledge and alleviate the inter-class sample imbalance issue. Thirdly, we introduce structural graph knowledge distillation to transfer the knowledge of historical tasks. Comprehensive experiments on three datasets validate the proposed method.