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Class-Incremental Learning: A Survey

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arxiv 2302.03648 v2 pith:TLG4ZJGR submitted 2023-02-07 cs.CV cs.LG

Da-Wei Zhou , Qi-Wei Wang , Zhi-Hong Qi , Han-Jia Ye , De-Chuan Zhan , Ziwei Liu This is my paper · ORCID
classification cs.CVcs.LG
keywords learningclass-incrementalclassescomparisonmodelsurveybudgetcharacteristics
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Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs -- the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in class-incremental learning and summarize these methods from several aspects. We also provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures. The source code is available at https://github.com/zhoudw-zdw/CIL_Survey/

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

    cs.LG 2025-07 conditional novelty 5.0 of 10

    DCE trains frequency-aware experts with complementary losses plus a Gaussian-sampled dynamic selector, reporting SOTA accuracy on four imbalanced domain-incremental benchmarks.

  2. A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    UG-CLU derives a four-part gradient update for continual learning and unlearning and shows it outperforms task-level CLU methods on new fine-grained benchmarks.

  3. Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels

    cs.CV 2025-08 conditional novelty 4.0 of 10

    ICPL generates pseudo-labels by clustering embeddings with KMeans, keeps only confident ones, and uses them to train class-incremental models without human labels, losing about 10 points versus supervised CIL but beat...

  4. C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning

    cs.LG 2025-08 conditional novelty 4.0 of 10

    Adding zeroth- and first-order flatness penalties to continual learning losses yields small consistent accuracy gains across seven methods, with the gated C-Flat++ variant at roughly 30% of the update cost.

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