mlsanityData to 7 Oct 2026

Unsupervised Class-Incremental Learning Through Confusion

arXiv 2104.04450 ↗First posted 9 Apr 2021cs.LG cs.CVcited by 3

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Abstract (arXiv)

While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model must distinguish between learned and novel classes to properly include samples for training. We introduce a novelty detection method that leverages network confusion caused by training incoming data as a new class. We found that incorporating a class-imbalance during this detection method substantially enhances performance. The effectiveness of our approach is demonstrated across a set of image classification benchmarks: MNIST, SVHN, CIFAR-10, CIFAR-100, and CRIB.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 9 Apr 2021.

Builds on

27 of 41 references.

References · 27

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15 more
  1. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  2. 2019Large Scale Incremental Learningcited by 727
  3. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 711
  4. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
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  6. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  7. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  8. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  9. 2019Continual Unsupervised Representation Learningcited by 147
  10. 2020A Neural Dirichlet Process Mixture Model for Task-Free Continual Learningcited by 133
  11. 2017Deep Generative Dual Memory Network for Continual Learningcited by 59
  12. 2018Uncertainty in the Variational Information Bottleneckcited by 39
  13. 2019Unsupervised Progressive Learning and the STAM Architecturecited by 29
  14. 2018WAIC, but Why? Generative Ensembles for Robust Anomaly Detectioncited by 25
  15. 2018Novelty Detection with GANcited by 8

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022Memory Replay with Data Compression for Continual Learningcited by 80
  2. 2024AggSS: An Aggregated Self-Supervised Approach for Class-Incremental Learningcited by 0
  3. 2025Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labelscited by 0