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Self-Supervised Class Incremental Learning
[article]
2021
arXiv
pre-print
Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time, which discards data labels and the model's classifiers. To comprehensively
arXiv:2111.11208v1
fatcat:dfhl4nu4prh6jhggmur4mfzsa4