Ruixuan Gao

DiAPR: Dimensionally-Allocated Prototype Refinement for Non-Exemplar Class Incremental Learning

Non-Exemplar Class Incremental Learning (NECIL) strives to preserve classification performance in an evolving data stream without revisiting old-class exemplars. Current methods mitigate catastrophic forgetting by replaying and augmenting historical prototypes as surrogates for old classes. However,