Sparsity-Adaptive Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2609.14274v1
• Date: Sun, 13 Sep 2026 04:21:56 GMT
• Title: Sparsity-Adaptive Sharpness-Aware Minimization
• Abstract summary: We introduce Sparsity-Adaptive Sharpness-Aware Minimization (SA-SAM), which derives a sparsity-dependent SAM/ASAM perturbation radius.<n>Across CIFAR-10-C, CIFAR-100-C, and ImageNet-100-C, our approach achieved stronger corruption robustness than the considered pruning baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deploying deep neural networks in real-world settings requires models that are both compact and robust to common corruptions. However, at deployment-relevant high sparsity, standard pruning pipelines often degrade corruption robustness, and existing sharpness-aware training/pruning approaches provide limited robustness gains. We address this issue by introducing Sparsity-Adaptive Sharpness-Aware Minimization (SA-SAM), which derives a sparsity-dependent SAM/ASAM perturbation radius by keeping the mean absolute perturbation (an $\ell_1$-based proxy) approximately invariant as sparsity increases. As a simple complementary option, we evaluate Magnitude-Weighted Hessian (MWH), derived from a second-order removal-path analysis, yielding an importance proportional to $\mathrm{Diag}(F)_i\,|w_i|$, where $\mathrm{Diag}(F)$ is the diagonal empirical Fisher used as a curvature proxy in our implementation. Across CIFAR-10-C, CIFAR-100-C, and ImageNet-100-C, our approach achieved stronger corruption robustness than the considered pruning baselines at 80--90\% sparsity, while preserving clean accuracy. We additionally quantify the robustness--throughput trade-off by reporting measured inference throughput under sparse execution at deployment-relevant sparsity levels.

Related papers

• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Robustness, Cost, and Attack-Surface Concentration in Phishing Detection [0.0]
  Phishing detectors built on engineered website features attain near-perfect accuracy under i.i.d. evaluation.<n>We study this gap through a cost-aware evasion framework that models discrete, monotone feature edits under explicit attacker budgets.
  arXiv  Detail & Related papers  (2026-03-19T17:53:32Z)
• Sharpness Aware Surrogate Training for Spiking Neural Networks [0.0]
  Surrogate gradients are a standard tool for training spiking neural networks (SNNs)<n>But conventional hard forward or surrogate backward training couples a nonsmooth forward model with a biased estimator.<n>We apply sharpness aware Surrogate Training (SAST), which applies sharpness aware contractionmization (SAM) to surrogate forward SNN trained by backpropagation.
  arXiv  Detail & Related papers  (2026-03-14T01:26:26Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Adaptive Sample-Level Framework Motivated by Distributionally Robust Optimization with Variance-Based Radius Assignment for Enhanced Neural Network Generalization Under Distribution Shift [0.8101875496469488]
  Distribution shifts and minority subpopulations frequently undermine the reliability of deep neural networks trained using Empirical Risk Minimization (ERM)<n>We propose a variance-driven, sample-level DRO framework that automatically identifies high-risk training samples and assigns a personalized robustness budget to each based on its online loss variance.
  arXiv  Detail & Related papers  (2025-11-04T10:20:21Z)
• CR-SAM: Curvature Regularized Sharpness-Aware Minimization [8.248964912483912]
  Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. In this paper, we introduce a normalized Hessian trace to accurately measure the curvature of loss landscape on em both training and test sets. In particular, to counter excessive non-linearity of loss landscape, we propose Curvature Regularized SAM (CR-SAM)
  arXiv  Detail & Related papers  (2023-12-21T03:46:29Z)
• Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer [158.2634766682187]
  Deep neural networks often suffer from poor generalization due to complex and non- unstructured loss landscapes. SharpnessAware Minimization (SAM) is a popular solution that smooths the loss by minimizing the change of landscape when adding a perturbation. In this paper, we propose Sparse SAM (SSAM), an efficient and effective training scheme that achieves perturbation by a binary mask.
  arXiv  Detail & Related papers  (2023-06-30T09:33:41Z)
• Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach [132.37966970098645]
  One of the popular solutions is Sharpness-Aware Minimization (SAM), which minimizes the change of weight loss when adding a perturbation. In this paper, we propose an efficient effective training scheme coined as Sparse SAM (SSAM), which achieves double overhead of common perturbations. In addition, we theoretically prove that S can converge at the same SAM, i.e., $O(log T/sqrtTTTTTTTTTTTTTTTTT
  arXiv  Detail & Related papers  (2022-10-11T06:30:10Z)
• Removing Batch Normalization Boosts Adversarial Training [83.08844497295148]
  Adversarial training (AT) defends deep neural networks against adversarial attacks. A major bottleneck is the widely used batch normalization (BN), which struggles to model the different statistics of clean and adversarial training samples in AT. Our normalizer-free robust training (NoFrost) method extends recent advances in normalizer-free networks to AT.
  arXiv  Detail & Related papers  (2022-07-04T01:39:37Z)
• Sharpness-Aware Training for Free [163.1248341911413]
  SharpnessAware Minimization (SAM) has shown that minimizing a sharpness measure, which reflects the geometry of the loss landscape, can significantly reduce the generalization error. Sharpness-Aware Training Free (SAF) mitigates the sharp landscape at almost zero computational cost over the base. SAF ensures the convergence to a flat minimum with improved capabilities.
  arXiv  Detail & Related papers  (2022-05-27T16:32:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.