arXiv:2601. 04539v2 Announce Type: replace-cross Abstract: In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning.
By Noah Eckstein, Manoj Srinivasan
arXiv:2505. 22839v2 Announce Type: replace-cross Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models.
By Liu Yuezhang, Xue-Xin Wei
arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.
By Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno
arXiv:2606. 31581v1 Announce Type: new Abstract: We investigate the problem of the robustness of a trained neural network to the perturbation of its input values.
By Mark Levene, Martyn Harris
arXiv:2508. 09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios.
By Xinlei Zhang, Fan Liu, Chuanyi Zhang, Fan Cheng, Qian Li, Yuhui Zheng
arXiv:2606. 01437v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications.
By Daniel Sadig, Mohammadreza Maleki, Hamed Karimi, Reza Samavi