Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks

• URL: http://arxiv.org/abs/2412.16854v1
• Date: Sun, 22 Dec 2024 04:40:02 GMT
• Title: Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks
• Authors: Jinping Zou, Xiaoge Deng, Tao Sun,
• Abstract summary: Sharpness-Aware Minimization (SAM) has proven highly effective in improving model generalization in machine learning tasks. We propose the SAM with Adaptive Regularization (SAMAR), which introduces a flexible sharpness ratio rule to update the regularization parameter dynamically.
• Score: 4.877624278656814
• License:
• Abstract: Sharpness-Aware Minimization (SAM) has proven highly effective in improving model generalization in machine learning tasks. However, SAM employs a fixed hyperparameter associated with the regularization to characterize the sharpness of the model. Despite its success, research on adaptive regularization methods based on SAM remains scarce. In this paper, we propose the SAM with Adaptive Regularization (SAMAR), which introduces a flexible sharpness ratio rule to update the regularization parameter dynamically. We provide theoretical proof of the convergence of SAMAR for functions satisfying the Lipschitz continuity. Additionally, experiments on image recognition tasks using CIFAR-10 and CIFAR-100 demonstrate that SAMAR enhances accuracy and model generalization.

Related papers

• Avoiding spurious sharpness minimization broadens applicability of SAM [13.21265875272573]
  Curvature regularization techniques like Sharpness Aware Minimization (SAM) have shown great promise in improving generalization on vision tasks. We find that SAM performs poorly in domains like natural language processing (NLP), often degrading performance -- even with twice the compute budget. We develop an alternative algorithm we call Functional-SAM, which regularizes curvature only through modification of the statistics of the overall function.
  arXiv  Detail & Related papers  (2025-02-04T15:25:47Z)
• Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning [63.55145330447408]
  We propose a novel textbfSelf-textbfPerceptinon textbfTuning (textbfSPT) method for anomaly segmentation. The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process.
  arXiv  Detail & Related papers  (2024-11-26T08:33:25Z)
• μP$^2$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling [49.25546155981064]
  We study the infinite-width limit of neural networks trained with Sharpness Aware Minimization (SAM) Our findings reveal that the dynamics of standard SAM effectively reduce to applying SAM solely in the last layer in wide neural networks. In contrast, we identify a stable parameterization with layerwise scaling, which we call $textitMaximal Update and Perturbation $ ($mu$P$2$), that ensures all layers are both feature learning and effectively perturbed in the limit.
  arXiv  Detail & Related papers  (2024-10-31T16:32:04Z)
• Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems [26.377807940655305]
  This work introduces a concept termed balancedness, defined as the difference between the squared norm of two variables. We develop a resource-efficient SAM variant, balancedness-aware regularization (BAR), tailored for scale-invariant problems.
  arXiv  Detail & Related papers  (2024-10-18T18:19:18Z)
• Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy [12.050160495730381]
  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.
  arXiv  Detail & Related papers  (2024-01-14T10:53:36Z)
• Understanding Self-attention Mechanism via Dynamical System Perspective [58.024376086269015]
  Self-attention mechanism (SAM) is widely used in various fields of artificial intelligence. We show that intrinsic stiffness phenomenon (SP) in the high-precision solution of ordinary differential equations (ODEs) also widely exists in high-performance neural networks (NN) We show that the SAM is also a stiffness-aware step size adaptor that can enhance the model's representational ability to measure intrinsic SP.
  arXiv  Detail & Related papers  (2023-08-19T08:17:41Z)
• Normalization Layers Are All That Sharpness-Aware Minimization Needs [53.799769473526275]
  Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima. We show that perturbing only the affine normalization parameters (typically comprising 0.1% of the total parameters) in the adversarial step of SAM can outperform perturbing all of the parameters.
  arXiv  Detail & Related papers  (2023-06-07T08:05:46Z)
• 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)
• 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)
• How Does Sharpness-Aware Minimization Minimize Sharpness? [29.90109733192208]
  Sharpness-Aware Minimization (SAM) is a highly effective regularization technique for improving the generalization of deep neural networks. This paper rigorously nails down the exact sharpness notion that SAM regularizes and clarifies the underlying mechanism.
  arXiv  Detail & Related papers  (2022-11-10T17:56:38Z)
• 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.