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Multi-view prototype balance and temporary proxy constraint for exemplar-free class-incremental learning

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Abstract

Exemplar-free class-incremental learning recognizes both old and new classes without saving old class exemplars because of storage limitations and privacy constraints. To address the forgetting of knowledge caused by the absence of old training data, we present a novel method that consists of two modules, multi-view prototype balance and temporary proxy constraints, which are based on feature retention and representation optimization. Specifically, multi-view prototype balance first extends the prototypes to maintain the general state of the class and then balances these prototypes combining knowledge distillation and prototype compensation to ensure the stability and plasticity of the model. To alleviate the feature overlap, the proposed temporary proxy constraint sets the temporary proxies to lightly compress the feature distribution during each mini-batch of training. Extensive experiments on five datasets with different settings demonstrate the superiority of our method against the state-of-the-art exemplar-free class-incremental learning methods.

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Data Availability and Access

The datasets generated during and/or analyzed during the current study are available in the [CIFAR-100 dataset] with [https://www.cs.toronto.edu/~kriz/cifar.html], [Tiny-ImageNet dataset] with [https://www.kaggle.com/c/tiny-imagenet], [ImageNet-Sub dataset] with [https://www.kaggle.com/datasets/ambityga/imagenet100], and [CUB dataset, ImageNet-R] with [https://github.com/zhoudw-zdw/RevisitingCIL].

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Acknowledgements

This work is supported by Natural Science Foundation of China under Grant No. 62476087, the National Key Research and Development Program of China (2022YFB3203500), Shanghai Science and Technology Program “Federated based cross-domain and cross-task incremental learning” under Grant No. 21511100800, Natural Science Foundation of China under Grant No. 62002193, Chinese Defense Program of Science and Technology under Grant No.2021-JCJQ-JJ-0041, China Aerospace Science and Technology Corporation Industry-University-Research Cooperation Foundation of the Eighth Research Institute under Grant No.SAST2021-007.

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, P.R. China

    Heng Tian, Qian Zhang, Zhe Wang, Yu Zhang, Xinlei Xu & Zhiling Fu

  2. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, P.R. China

    Heng Tian, Qian Zhang, Zhe Wang, Yu Zhang, Xinlei Xu & Zhiling Fu

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  1. Heng Tian
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Contributions

Heng Tian: Methodology, original draft review and editing, experiment; Qiang Zhang: Validation, reviewing and proofreading; Zhe Wang: Project administration, reviewing and proofreading; Yu Zhang: Validation, investigation. Xinlei Xu and Zhiling Fu: Investigation and proofreading.

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Correspondence to Qian Zhang or Zhe Wang.

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Written informed consent for publication of this paper was obtained from the Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, and all authors.

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Tian, H., Zhang, Q., Wang, Z. et al. Multi-view prototype balance and temporary proxy constraint for exemplar-free class-incremental learning. Appl Intell 55, 344 (2025). https://doi.org/10.1007/s10489-025-06233-7

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