mlsanityData to 7 Oct 2026

Domain Invariant Adversarial Learning

arXiv 2104.00322 ↗First posted 1 Apr 2021cs.LG cs.CVcited by 7

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Abstract (arXiv)

The phenomenon of adversarial examples illustrates one of the most basic vulnerabilities of deep neural networks. Among the variety of techniques introduced to surmount this inherent weakness, adversarial training has emerged as the most effective strategy for learning robust models. Typically, this is achieved by balancing robust and natural objectives. In this work, we aim to further optimize the trade-off between robust and standard accuracy by enforcing a domain-invariant feature representation. We present a new adversarial training method, Domain Invariant Adversarial Learning (DIAL), which learns a feature representation that is both robust and domain invariant. DIAL uses a variant of Domain Adversarial Neural Network (DANN) on the natural domain and its corresponding adversarial domain. In the case where the source domain consists of natural examples and the target domain is the adversarially perturbed examples, our method learns a feature representation constrained not to discriminate between the natural and adversarial examples, and can therefore achieve a more robust representation. DIAL is a generic and modular technique that can be easily incorporated into any adversarial training method. Our experiments indicate that incorporating DIAL in the adversarial training process improves both robustness and standard accuracy.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 72 of its 97 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

72 of 97 references.

References · 72

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38 more
  1. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  2. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  3. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
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  5. 2018Robustness May Be at Odds with Accuracycited by 873
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  7. 2020Fast is better than free: Revisiting adversarial trainingcited by 723
  8. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
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  10. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 591
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  12. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  13. 2020Overfitting in adversarially robust deep learningcited by 504
  14. 2019Unlabeled Data Improves Adversarial Robustnesscited by 415
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  17. 2020Do Adversarially Robust ImageNet Models Transfer Better?cited by 315
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  22. 2018Adversarial Logit Pairingcited by 224
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  25. 2021Fixing Data Augmentation to Improve Adversarial Robustnesscited by 201
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  27. 2018Spatially Transformed Adversarial Examplescited by 195
  28. 2019You Only Propagate Once: Accelerating Adversarial Training via Maximal Principlecited by 181
  29. 2018Semidefinite relaxations for certifying robustness to adversarial examplescited by 178
  30. 2018MMA Training: Direct Input Space Margin Maximization through Adversarial Trainingcited by 171
  31. 2019Are Labels Required for Improving Adversarial Robustness?cited by 162
  32. 2020Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuningcited by 152
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  36. 2019Adversarially Robust Distillationcited by 124
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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2.

7 papers.

  1. 2023Cross-Entropy Loss Functions: Theoretical Analysis and Applicationscited by 206
  2. 2022A Characterization of Semi-Supervised Adversarially-Robust PAC Learnabilitycited by 13
  3. 2022Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptationcited by 7
  4. 2021Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustnesscited by 6
  5. 2023Splitting the Difference on Adversarial Trainingcited by 4
  6. 2024M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Schedulingcited by 0
  7. 2024DomainLab: A modular Python package for domain generalization in deep learningcited by 0