Dual distillation and distribution-aware memory replay with self-learning for class-incremental learning
A dual knowledge distillation with clustering-based memory selection for class-incremental learning is proposed, which excavates the multi-modal structure within each class to improve the coverage of the replayed samples over the true data distribution and enhance the robustness of the memory buffer to feature drift.