Relevant bibliographies by topics / Adaptive Sharpness Aware Minimization

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Academic literature on the topic 'Adaptive Sharpness Aware Minimization'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Adaptive Sharpness Aware Minimization"

1

Ratchatorn, Tanapat, and Masayuki Tanaka. "Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss." IEEE Access 13 (2025): 100326–37. https://doi.org/10.1109/access.2025.3578265.

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Dong, Mingrong, Yixuan Yang, Kai Zeng, Qingwang Wang, and Tao Shen. "Implicit Sharpness-Aware Minimization for Domain Generalization." Remote Sensing 16, no. 16 (2024): 2877. http://dx.doi.org/10.3390/rs16162877.

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Domain generalization (DG) aims to learn knowledge from multiple related domains to achieve a robust generalization performance in unseen target domains, which is an effective approach to mitigate domain shift in remote sensing image classification. Although the sharpness-aware minimization (SAM) method enhances DG capability and improves remote sensing image classification performance by promoting the convergence of the loss minimum to a flatter loss surface, the perturbation loss (maximum loss within the neighborhood of a local minimum) of SAM fails to accurately measure the true sharpness o
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Wu, Tao, Tie Luo, and Donald C. Wunsch II. "CR-SAM: Curvature Regularized Sharpness-Aware Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 6144–52. http://dx.doi.org/10.1609/aaai.v38i6.28431.

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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
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Xing, Xinda, Qiugang Zhan, Xiurui Xie, Yuning Yang, Qiang Wang, and Guisong Liu. "Flexible Sharpness-Aware Personalized Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21707–15. https://doi.org/10.1609/aaai.v39i20.35475.

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Personalized federated learning (PFL) is a new paradigm to address the statistical heterogeneity problem in federated learning. Most existing PFL methods focus on leveraging global and local information such as model interpolation or parameter decoupling. However, these methods often overlook the generalization potential during local client learning. From a local optimization perspective, we propose a simple and general PFL method, Federated learning with Flexible Sharpness-Aware Minimization (FedFSA). Specifically, we emphasize the importance of applying a larger perturbation to critical laye
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Deng, Jiaxin, Junbiao Pang, Baochang Zhang, and Guodong Guo. "Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16208–16. https://doi.org/10.1609/aaai.v39i15.33780.

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Sharpness-Aware Minimization (SAM) has emerged as a promising approach for effectively reducing the generalization error. However, SAM incurs twice the computational cost compared to the base optimizer (e.g., SGD). We propose Asymptotic Unbiased data sampling to accelerate SAM (AUSAM), which maintains the model's generalization capacity while significantly enhancing computational efficiency. Concretely, we probabilistically sample a subset of data points beneficial for SAM optimization based on a theoretically guaranteed criterion, i.e., the Gradient Norm of each Sample (GNS). We further appro
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Zhou, Changbao, Jiawei Du, Ming Yan, Hengshan Yue, Xiaohui Wei, and Joey Tianyi Zhou. "SAR: Sharpness-Aware minimization for enhancing DNNs’ Robustness against bit-flip errors." Journal of Systems Architecture 156 (November 2024): 103284. http://dx.doi.org/10.1016/j.sysarc.2024.103284.

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Mariam, Iqra, Xiaorong Xue, and Kaleb Gadson. "A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model." Sensors 24, no. 13 (2024): 4267. http://dx.doi.org/10.3390/s24134267.

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Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet’s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model
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Liang, Hailun, Haowen Zheng, Hao Wang, Liu He, Haoyi Lin, and Yanyan Liang. "Exploring Flatter Loss Landscape Surface via Sharpness-Aware Minimization with Linear Mode Connectivity." Mathematics 13, no. 8 (2025): 1259. https://doi.org/10.3390/math13081259.

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The Sharpness-Aware Minimization (SAM) optimizer connects flatness and generalization, suggesting that loss basins with lower sharpness are correlated with better generalization. However, SAM requires manually tuning the open ball radius, which complicates its practical application. To address this, we propose a method inspired by linear connectivity, using two models initialized differently as endpoints to automatically determine the optimal open ball radius. Specifically, we introduce distance regularization between the two models during training, which encourages them to approach each other
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Wei, Zheng, Xingjun Zhang, and Zhendong Tan. "Unifying and revisiting Sharpness-Aware Minimization with noise-injected micro-batch scheduler for efficiency improvement." Neural Networks 185 (May 2025): 107205. https://doi.org/10.1016/j.neunet.2025.107205.

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Jeong, In-Woong, Han-Jin Lee, Jae-Hwan Jeong, and Seok-Hwan Choi. "A Study on Malware Family Classification Method based on Separable Vision Transformer Using Sharpness-Aware Minimization." Journal of Korean Institute of Intelligent Systems 34, no. 4 (2024): 329–38. http://dx.doi.org/10.5391/jkiis.2024.34.4.329.

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Book chapters on the topic "Adaptive Sharpness Aware Minimization"

1

Bakurov, Illya, Nathan Haut, and Wolfgang Banzhaf. "Sharpness-Aware Minimization in Genetic Programming." In Genetic and Evolutionary Computation. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0077-9_8.

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Khanh, Pham Duy, Hoang-Chau Luong, Boris S. Mordukhovich, Dat Ba Tran, and Truc Vo. "Convergence of Sharpness-Aware Minimization with Momentum." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73420-5_11.

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Li, Dongqi, Zhu Teng, Qirui Li, and Ziyin Wang. "Sharpness-Aware Minimization for Out-of-Distribution Generalization." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_43.

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Wang, Xuehao, Weisen Jiang, Shuai Fu, and Yu Zhang. "Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70344-7_22.

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Wu, Chenyu, Ting Zhang, and ZhiXian Li. "SAM-FT: Enhanced Generalizable Medical Image Segmentation via Sharpness-Aware Minimization and Focal Loss." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6688-1_7.

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Ibayashi, Hikaru, Taufeq Mohammed Razakh, Liqiu Yang, et al. "Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_12.

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Wu, Lilei, Zhen Wang, and Jie Liu. "Adaptive Self-Supervised Continual Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230576.

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Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and dat
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Conference papers on the topic "Adaptive Sharpness Aware Minimization"

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Ratchatorn, Tanapat, and Masayuki Tanaka. "Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware Minimization." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647582.

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Zou, Jinping, Xiaoge Deng, and Tao Sun. "Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890114.

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Li, Tao, Pan Zhou, Zhengbao He, Xinwen Cheng, and Xiaolin Huang. "Friendly Sharpness-Aware Minimization." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00538.

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Zhang, Fengchun, Dongfen Li, Jinshan Lai, Yang Zhang, Fengli Zhang, and Ruijin Wang. "General Dynamic Regularization Federated Learning with Hybrid Sharpness-Aware Minimization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890601.

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Zhang, Yilang, Bingcong Li, and Georgios B. Giannakis. "Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889586.

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Nguyen, Tung, Tue Le, Hoang Tran Vuong, et al. "Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.231.

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Wu, Chenyu, Ting Zhang, Zhixian Li, and Zhichao Lian. "Enhancing Adversarial Patch Effectiveness for Face Recognition Systems Using Sharpness-Aware Minimization." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00066.

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Zareno, Kaitlin, Jarett C. Dewbury, Siamak K. Sorooshyari, Hossein Mobahi, and Loza F. Tadesse. "Sharpness-aware minimization (SAM) improves generalization performance of bacterial Raman spectral data enabling portable diagnostics." In Optics and Biophotonics in Low-Resource Settings XI, edited by David Levitz and Aydogan Ozcan. SPIE, 2025. https://doi.org/10.1117/12.3049180.

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Henwood, Sébastien, Gonçalo Mordido, Yvon Savaria, Sarath Chandar, and François Leduc-Primeau. "Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10943038.

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Liu, Hailong, and Wenbi Rao. "Supervised contrastive probability learning and sharpness-aware minimization model for multimodal emotion recognition in conversations." In Fourth International Conference on Algorithms, Microchips, and Network Applications (AMNA 2025), edited by Javid Taheri and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3068505.

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