March 11, 2024, 4:44 a.m. | Zichong Meng, Jie Zhang, Changdi Yang, Zheng Zhan, Pu Zhao, Yanzhi WAng

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.05016v1 Announce Type: new
Abstract: Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data. Recent exemplar-free CIL methods attempt to mitigate catastrophic forgetting by synthesizing previous task data. However, they fail to overcome the catastrophic forgetting due to the inability to deal with the significant domain gap between real and synthetic data. To overcome these issues, we propose a …

abstract arxiv catastrophic forgetting class cs.cv data diffusion free however incremental type

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