Retrospective Class Incremental Learning

Existing works study the Class Incremental learning (CIL) problem with the assumption that the data for previous classes are absent, or only a small subset of samples (known as exemplars) are accessible. Differently, we propose a new and practical setting called retrospective CIL, where all the previous data are accessible, but with bounded training budgets for old data replay. Since only a small subset of old samples can be replayed, it brings a new research problem, i.e., dynamically sampling old data along the incremental training process. As incremental learning particularly suffers from catastrophic forgetting, we propose to use the forgettability of the old samples as the sampling priorities to favour the forgotten samples during the dynamic sampling process. To achieve this, we introduce a forgetting rate metric with graph-based propagation to estimate the sample forgettability. The proposed method brings improvements on two benchmark datasets.