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Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
March 28, 2024, 4:45 a.m. | Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
cs.CV updates on arXiv.org arxiv.org
Abstract: Deep neural networks perform remarkably well in close-world scenarios. However, novel classes emerged continually in real applications, making it necessary to learn incrementally. Class-incremental learning (CIL) aims to gradually recognize new classes while maintaining the discriminability of old ones. Existing CIL methods have two limitations: a heavy reliance on preserving old data for forgetting mitigation and the need for vast labeled data for knowledge adaptation. To overcome these issues, we propose a non-exemplar semi-supervised CIL …
abstract applications arxiv class cs.cv however incremental learn limitations making networks neural networks novel reliance semi-supervised type world
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