mlsanityData to 7 Oct 2026

Towards Adversarially Robust Continual Learning

arXiv 2303.17764 ↗First posted 31 Mar 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world. Deep learning models, however, are shown to be vulnerable to adversarial attacks. Though there are many studies on the model robustness in the context of standard supervised learning, protecting continual learning from adversarial attacks has not yet been investigated. To fill in this research gap, we are the first to study adversarial robustness in continual learning and propose a novel method called Task-Aware Boundary Augmentation (TABA) to boost the robustness of continual learning models. With extensive experiments on CIFAR-10 and CIFAR-100, we show the efficacy of adversarial training and TABA in defending adversarial attacks.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 31 Mar 2023.

Builds on

17 of 27 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  6. 2013Intriguing properties of neural networkscited by 5,114
  7. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  8. 2016Learning without Forgettingcited by 2,465
  9. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  10. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  11. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  12. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
5 more
  1. 2017Overcoming Catastrophic Forgetting by Incremental Moment Matchingcited by 243
  2. 2020Geometry-aware Instance-reweighted Adversarial Trainingcited by 198
  3. 2022Federated Class-Incremental Learningcited by 126
  4. 2020Neural Topic Modeling with Continual Lifelong Learningcited by 24
  5. 2022Susceptibility of Continual Learning Against Adversarial Attackscited by 8

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 5.

8 papers.

  1. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  2. 2025SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devicescited by 8
  3. 2024Maintaining Adversarial Robustness in Continuous Learningcited by 5
  4. 2025Adapting to Evolving Adversaries with Regularized Continual Robust Trainingcited by 2
  5. 2024Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learningcited by 0
  6. 2025Noise-Tolerant Coreset-Based Class Incremental Continual Learningcited by 0
  7. 2025Exploring Cross-Stage Adversarial Transferability in Class-Incremental Continual Learningcited by 0
  8. 2025SoK: Systematic analysis of adversarial threats against deep learning approaches for autonomous anomaly detection systems in SDN-IoT networkscited by 0