June 6, 2024, 4:42 a.m. | Qiang Nie, Weifu Fu, Yuhuan Lin, Jialin Li, Yifeng Zhou, Yong Liu, Lei Zhu, Chengjie Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03065v1 Announce Type: new
Abstract: Instance-incremental learning (IIL) focuses on learning continually with data of the same classes. Compared to class-incremental learning (CIL), the IIL is seldom explored because IIL suffers less from catastrophic forgetting (CF). However, besides retaining knowledge, in real-world deployment scenarios where the class space is always predefined, continual and cost-effective model promotion with the potential unavailability of previous data is a more essential demand. Therefore, we first define a new and more practical IIL setting as …

abstract arxiv catastrophic forgetting class consolidation continual cs.cv cs.lg data decision deployment however incremental incremental learning instance knowledge space type world

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