PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization

arXiv:2610.01807 · cs.CV, cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/ahmed-sharshar/PhaseAT

Terminology

• Generalization — 27× in this paper · explained in 13 episodes
• Domain Generalization — 24× in this paper
• Robustness — 13× in this paper · explained in 5 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Adversarial Training — 9× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Single Domain Generalization — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• International Conference on Learning Representations — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Data Augmentation — 1× in this paper · explained in 1 episode
• Adversarial Defense — 1× in this paper · explained in 1 episode
• Adversarial Training (AT) — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
• In Search of Lost Domain Generalization
• Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization

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