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Mark Horton

2 accepted papers

2025

Boosting Adversarial Robustness with CLAT: Criticality Leveraged Adversarial Training

ICML 2025poster

Adversarial training (AT) enhances neural network robustness. Typically, AT updates all trainable parameters, but can lead to overfitting and increased errors on clean data. Research suggests that fine-tuning specific parameters may be more effective; however, methods for identifying these essential…

Cited by 0SourcePDFScholar
2025

SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers

ICCV 2025poster

Vision transformers (ViTs) have become essential backbones in advanced computer vision applications and multi-modal foundation models. Despite their strengths, ViTs remain vulnerable to adversarial perturbations, comparable to or even exceeding the vulnerability of convolutional neural networks (CNN…

Cited by 0SourcePDFScholar