The paper investigates whether the stable GAN architecture R3GAN can improve time‑series imputation when adapted to 1‑D temporal data. Using a coarse‑to‑fine refinement framework and a frequency‑domain discriminator, the authors evaluate 14 saved configurations across three datasets and find a negative result: most configurations either show negligible improvement or degrade performance compared to baseline methods. The study highlights that the usual argument—GANs optimize distributional objectives rather than point‑wise ones—does not fully explain the lack of benefit, and it poses an open problem regarding why a learned discriminator fails to provide useful refinement gradients while diffusion denoisers succeed, offering practical guidance on when adversarial refinement may be worthwhile.
By Yufeng He
arXiv:2610.15359v1 Announce Type: cross
Abstract: Deep classifiers deployed in safety-critical services like autonomous-driving perception remain vulnerable to small adversarial perturbations. Certif...
By Rui Zhang, Xinle Cao, Huazhen Yu, Jian Liu, Youwen Zhu
The paper introduces topology‑inspired fine‑tuning for wind‑field super‑resolution, adding auxiliary losses based on persistence diagrams and merge‑tree distances to a pretrained CNN. A comprehensive study of 16 fine‑tuning setups shows that gradient supervision improves bottleneck persistence‑diagram metrics, while fixed‑pair supervision yields the most consistent gains in both persistence‑diagram and merge‑tree distances. The combined objective reduces key topological distances by up to 31% and improves PSNR by 1.30 dB compared to the original CNN.
By Arjun Dadhwal, Brian Summa
arXiv:2604. 21395v3 Announce Type: replace-cross Abstract: Ordinary supervised training minimises the task loss and then stops.
By Vishal Rajput
The paper investigates why neural network surrogates sometimes improve and sometimes worsen derivative‑free optimisation performance. It identifies three key factors—role (whether the surrogate proposes candidates or replaces gradients), radius (the neighbourhood within which a local model is reliable), and room (whether the base method can still progress)—that determine when a learned local model is beneficial. Experiments on 117 benchmark instances show that providing surrogate‑approved candidates boosts success rates, while replacing gradients or ignoring the radius can reduce them.
By Chengkuo Bian, Pengcheng Xie
The paper introduces Sparsity-Adaptive Sharpness-Aware Minimization (SA‑SAM), a method that adjusts the perturbation radius in sharpness-aware training to remain consistent as model sparsity increases. It also evaluates a Magnitude‑Weighted Hessian (MWH) importance metric derived from second‑order analysis. Experiments on CIFAR‑10‑C, CIFAR‑100‑C, and ImageNet‑100‑C show that SA‑SAM improves corruption robustness at 80–90% sparsity while maintaining clean accuracy, and the study reports inference throughput at deployment‑relevant sparsity levels.
By Shiryu Ueno, Yoshikazu Hayashi, Kunihito Kato
arXiv:2605.06240v2 Announce Type: replace-cross
Abstract: Forward-Forward (FF) training lets each layer learn from a local goodness criterion. In cumulative-goodness variants, later layers can inheri...
By Amirhossein Yousefiramandi
arXiv:2610.18227v1 Announce Type: cross
Abstract: Domain-specific curvature is an appealing idea for domain generalization (DG), but curvature learning can denote substantially different computations...
By Sunny Gupta, Amit Sethi
The paper introduces Stiefel Attention, which constrains the query and key projection matrices of transformers to the Stiefel manifold and optimizes them with a Riemannian Adam variant. It demonstrates that this approach yields steepest‑descent updates, is well‑conditioned, and preserves learned attention geometry during weight decay. Empirical results show significant accuracy gains on modular arithmetic grokking and CIFAR‑10 patches, with the improvement attributed to a step‑scale‑free update rule rather than equivariance or projector changes.
By Rub\'en Dar\'io Guerrero
The paper investigates why direct pairwise AUC surrogate training, though aligned with the ranking metric, can fail to provide corrective updates during stochastic training. It introduces the concept of Corrective Power, showing that in certain regions severe ranking errors persist without generating strong gradient signals, a phenomenon linked to degeneracies in the U‑statistics of pairwise gradients. The authors explain empirical observations—such as the effectiveness of squared‑hinge loss, the benefit of cross‑entropy warm‑up, and the advantage of parameter‑efficient fine‑tuning—through this theoretical lens, and validate their claims with experiments on eight computer‑vision and eight NLP tasks using various models.
By Jia Chen, Zhiyong Yang, Shilong Bao, Qianqian Xu, Qingming Huang
The paper introduces a method for adversarial training that avoids computing input gradients by using a low‑rank Householder expansion (LRHE) to directly generate small‑norm adversarial examples from a network’s parameters. This approach requires only forward passes and standard back‑propagation, eliminating the inner maximization loop and reducing computational cost to roughly 2.8 PGD steps per epoch. The resulting models achieve comparable robustness to multi‑step PGD training for small relative ε budgets, demonstrating the feasibility of gradient‑free adversarial training.
By Tiana C. Johnson, Donsub Rim
arXiv:2606. 27771v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training improves the reward alignment of flow-based generators, but often degrades perceptual quality in ways that are not captured by the reward proxy.
By Tianlin Pan, Lianyu Pang, Cheng Da, Huan Yang, Changqian Yu, Kun Gai, Wenhan Luo