Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations

To tackle the susceptibility of deep neural networks to examples, the adversarial training has been proposed which provides a notion of robust through an inner maximization problem presenting the first-order embedded within the outer minimization of the training loss. To generalize the adversarial robustness over different perturbation types, the adversarial training method has been augmented with the improved inner maximization presenting a union of multiple perturbations e.g., various $\ell_p$ norm-bounded perturbations.

@article{cheng2025_2104.10586,
  title={ Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations },
  author={ Hao Cheng and Kaidi Xu and Chenan Wang and Bhavya Kailkhura and Xue Lin and Ryan Goldhahn },
  journal={arXiv preprint arXiv:2104.10586},
  year={ 2025 }
}