Rethinking Fast Adversarial Training: A Splitting Technique to Overcome Catastrophic Overfitting

Masoumeh Zareapoor, Pourya Shamsolmoali
2/14/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

DOISemantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Toward Generalizing Visual Brain Decoding to Unseen Subjects

97% match1 repo292026

Transferring Labels to Solve Annotation Mismatches Across Object Detection Datasets

96% match2026

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?

96% match2026

Learning to Solve Differential Equation Constrained Optimization Problems

96% match2026

Learning to Optimize Differentiable Games

95% match1 repo102026

Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation

95% match2026

Cite this paper

@article{zareapoor2026rethinking,
  title  = {Rethinking Fast Adversarial Training: A Splitting Technique to Overcome Catastrophic Overfitting},
  author = {Masoumeh Zareapoor and Pourya Shamsolmoali},
  year   = {2026},
  doi    = {10.1007/978-3-031-73229-4_3},
  url    = {https://doi.org/10.1007/978-3-031-73229-4_3},
  journal = {ECCV 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2e30437f9ac5b641d3acd99c2c4cf8d722d9299e.svg)](https://codeofpaper.com/paper/2e30437f9ac5b641d3acd99c2c4cf8d722d9299e)

Discussion