Boosting Knowledge Transfer and Retention with Brain-inspired Multi-View Incremental Learning

Abstract

Traditional multi-view learning models are primarily designed for static datasets with fixed views. However, in dynamic incremental view environments, this approach inevitably leads to view forgetting, where the introduction of new views weakens previously acquired knowledge. In contrast, the human brain exhibits remarkable memory retention and knowledge transfer capabilities when receiving objects described from different perspectives, with past experience further supplementing new insights. Inspired by underlying neural processing mechanisms, we propose a novel view incremental learning framework named Hebbian View Orthogonal Projection (HVOP). HVOP constructs a knowledge transfer space, where gradient updates are projected onto the orthogonal complement of historical representations, thereby mitigating interference between old and new views. By further incorporating recursive lateral connections and Hebbian learning rules, the proposed model imparts brain-like dynamic adaptability to the learning process, enhancing knowledge transfer and integration, thereby enabling stable knowledge transfer under evolving views. We validate HVOP on node classification tasks, demonstrating its superior performance in both knowledge retention and transfer compared to traditional methods. The results highlight the efficacy of biologically inspired mechanisms in mitigating the view forgetting phenomenon.