4 papers
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training
Alan Mitkiy, James Smith, Myungseo wong +3
Adversarial training is among the most effective strategies for defending deep neural networks against adversarial examples. A key limitation of existing adversarial training appro…
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
Hana Satou, Alan Mitkiy
Transfer learning across domains with distribution shift remains a fundamental challenge in building robust and adaptable machine learning systems. While adversarial perturbations…
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation
Hana Satou, Alan Mitkiy, Emma Collins +1
Transfer learning under domain shift remains a fundamental challenge due to the divergence between source and target data manifolds. In this paper, we propose MAADA (Manifold-Aware…
Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications
Hana Satou, Alan Mitkiy
The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientif…