arXiv Machine Learning
ExGrad: Beyond Suppression - Explicit Clean-Gradient Subtraction in Adversarial Training
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arXiv Machine Learning
Sep 22
When Does Adversarial Refinement Help? A Negative Result and Open Problem in Adapting R3GAN to Time Series Imputation
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.
arXiv AI
Oct 15
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification
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...
arXiv Machine Learning
Oct 16
Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution
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.
arXiv AI
Aug 7
Supervised Learning Has a Geometric Blind Spot
arXiv:2604. 21395v3 Announce Type: replace-cross Abstract: Ordinary supervised training minimises the task loss and then stops.
arXiv Machine Learning
Aug 27
Why and When Neural Networks Improve Local Approximation in Optimization
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.
arXiv Computer Vision
Sep 15
Sparsity-Adaptive Sharpness-Aware Minimization
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.