Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers. (arXiv:2201.01488v1 [cs.LG])

The exemplar-free class incremental learning requires classification models
to learn new class knowledge incrementally without retaining any old samples.
Recently, the framework based on parallel one-class classifiers (POC), which
trains a one-class classifier (OCC) independently for each category, has
attracted extensive attention, since it can naturally avoid catastrophic
forgetting. POC, however, suffers from weak discriminability and comparability
due to its independent training strategy for different OOCs. To meet this
challenge, we propose a new framework, named Discriminative and Comparable
One-class …

arxiv incremental learning