Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy

• URL: http://arxiv.org/abs/2401.07250v2
• Date: Tue, 10 Sep 2024 02:58:54 GMT
• Title: Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
• Authors: Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao,
• Abstract summary: sharpness-aware generalization (SAM) has attracted much attention because of its surprising effectiveness in improving performance. We propose a simple renormalization strategy, dubbed Stable SAM (SSAM), so that the gradient norm of the descent step maintains the same as that of the ascent step. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost.
• Score: 12.050160495730381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performance. However, compared to stochastic gradient descent (SGD), it is more prone to getting stuck at the saddle points, which as a result may lead to performance degradation. To address this issue, we propose a simple renormalization strategy, dubbed Stable SAM (SSAM), so that the gradient norm of the descent step maintains the same as that of the ascent step. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost. With elementary tools from convex optimization and learning theory, we also conduct a theoretical analysis of sharpness-aware training, revealing that compared to SGD, the effectiveness of SAM is only assured in a limited regime of learning rate. In contrast, we show how SSAM extends this regime of learning rate and then it can consistently perform better than SAM with the minor modification. Finally, we demonstrate the improved performance of SSAM on several representative data sets and tasks.

Related papers

• Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization [17.670203551488218]
  We propose Asymptotic Unbiased Sampling to accelerate Sharpness-Aware Minimization (AUSAM) AUSAM maintains the model's generalization capacity while significantly enhancing computational efficiency. As a plug-and-play, architecture-agnostic method, our approach consistently accelerates SAM across a range of tasks and networks.
  arXiv  Detail & Related papers  (2024-06-12T08:47:44Z)
• 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)
• AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks [76.90477930208982]
  Sharpness aware (SAM) has been extensively explored as it can generalize better for training deep neural networks. Integrating SAM with adaptive learning perturbation and momentum acceleration, dubbed AdaSAM, has already been explored. We conduct several experiments on several NLP tasks, which show that AdaSAM could achieve superior performance compared with SGD, AMS, and SAMsGrad.
  arXiv  Detail & Related papers  (2023-03-01T15:12:42Z)
• mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization [20.560184120992094]
  Sharpness-Aware Minimization technique modifies the fundamental loss function that steers gradient descent methods toward flatter minima. We extend a recently developed and well-studied general framework for flatness analysis to theoretically show that SAM achieves flatter minima than SGD, and mSAM achieves even flatter minima than SAM.
  arXiv  Detail & Related papers  (2023-02-19T23:27:12Z)
• SAM operates far from home: eigenvalue regularization as a dynamical phenomenon [15.332235979022036]
  The Sharpness Aware Minimization (SAM) algorithm has been shown to control large eigenvalues of the loss Hessian. We show that SAM provides a strong regularization of the eigenvalues throughout the learning trajectory. Our theory predicts the largest eigenvalue as a function of the learning rate and SAM radius parameters.
  arXiv  Detail & Related papers  (2023-02-17T04:51:20Z)
• Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization [14.40189851070842]
  Sharpness-Aware Minimization (SAM) modifies the underlying loss function to guide descent methods towards flatter minima. Recent work suggests that mSAM can outperform SAM in terms of test accuracy. This paper presents a comprehensive empirical evaluation of mSAM on various tasks and datasets.
  arXiv  Detail & Related papers  (2022-12-07T00:37:55Z)
• 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)
• 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)

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.