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cs.LG2024
Adversarial Training in Low-Label Regimes with Margin-Based Interpolation
Tian Ye, Rajgopal Kannan, Viktor Prasanna
Adversarial training has emerged as an effective approach to train robust neural network models that are resistant to adversarial attacks, even in low-label regimes where labeled d…
cs.CV2024
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification
Xu Wang, Tian Ye, Rajgopal Kannan +1
Deep Learning (DL) Models for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), while delivering improved performance, have been shown to be quite vulnerable to ad…
cs.CV2024
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Tian Ye, Rajgopal Kannan, Viktor Prasanna +1
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems. An…