rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
Neural networks are central to modern artificial intelligence, yet their training remains highly sensitive to data contamination. Standard neural classifiers are trained by minimizing the categorical cross-entropy loss, corresponding to maximum likelihood estimation under a multinomial model. While statistically efficient under ideal conditions, this approach is highly vulnerable to contaminated observations including label noises corrupting supervision in the output space, and adversarial perturbations inducing worst-case deviations in the input space. In this paper, we propose a unified and statistically grounded framework for robust neural classification that addresses both forms of co...
| Authors | Suryasis Jana, Abhik Ghosh |
|---|---|
| Primary category | stat.ML |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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