CR-SAM: Curvature Regularized Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2312.13555v2
• Date: Sat, 23 Dec 2023 07:15:23 GMT
• Title: CR-SAM: Curvature Regularized Sharpness-Aware Minimization
• Authors: Tao Wu, Tie Luo, and Donald C. Wunsch
• Abstract summary: 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)
• Score: 8.248964912483912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. However, as training progresses, the non-linearity of the loss landscape increases, rendering one-step gradient ascent less effective. On the other hand, multi-step gradient ascent will incur higher training cost. 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), integrating the normalized Hessian trace as a SAM regularizer. Additionally, we present an efficient way to compute the trace via finite differences with parallelism. Our theoretical analysis based on PAC-Bayes bounds establishes the regularizer's efficacy in reducing generalization error. Empirical evaluation on CIFAR and ImageNet datasets shows that CR-SAM consistently enhances classification performance for ResNet and Vision Transformer (ViT) models across various datasets. Our code is available at https://github.com/TrustAIoT/CR-SAM.

Related papers

• Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models [42.59948316941217]
  Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and generalization degradation. We propose a new algorithm named GraphSAM, which reduces the training cost of SAM and improves the generalization performance of graph transformer models.
  arXiv  Detail & Related papers  (2024-06-19T01:03:23Z)
• Friendly Sharpness-Aware Minimization [62.57515991835801]
  Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. We investigate the key role of batch-specific gradient noise within the adversarial perturbation, i.e., the current minibatch gradient. By decomposing the adversarial gradient noise components, we discover that relying solely on the full gradient degrades generalization while excluding it leads to improved performance.
  arXiv  Detail & Related papers  (2024-03-19T01:39:33Z)
• 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)
• 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)
• Towards Efficient and Scalable Sharpness-Aware Minimization [81.22779501753695]
  We propose a novel algorithm LookSAM that only periodically calculates the inner gradient ascent. LookSAM achieves similar accuracy gains to SAM while being tremendously faster. We are the first to successfully scale up the batch size when training Vision Transformers (ViTs)
  arXiv  Detail & Related papers  (2022-03-05T11:53:37Z)
• Efficient Sharpness-aware Minimization for Improved Training of Neural Networks [146.2011175973769]
  This paper proposes Efficient Sharpness Aware Minimizer (M) which boosts SAM s efficiency at no cost to its generalization performance. M includes two novel and efficient training strategies-StochasticWeight Perturbation and Sharpness-Sensitive Data Selection. We show, via extensive experiments on the CIFAR and ImageNet datasets, that ESAM enhances the efficiency over SAM from requiring 100% extra computations to 40% vis-a-vis bases.
  arXiv  Detail & Related papers  (2021-10-07T02:20:37Z)
• Sharpness-Aware Minimization for Efficiently Improving Generalization [36.87818971067698]
  We introduce a novel, effective procedure for simultaneously minimizing loss value and loss sharpness. Sharpness-Aware Minimization (SAM) seeks parameters that lie in neighborhoods having uniformly low loss. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-03T19:02:10Z)

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.