New BSAM technique finds flatter neural network minima

Researchers have introduced Bilateral Sharpness-Aware Minimization (BSAM), a novel optimization technique designed to achieve flatter minima in neural networks. Unlike existing methods that primarily focus on the maximum sharpness in the gradient ascent direction, BSAM also considers the minimum sharpness within the surrounding neighborhood. This dual approach, merging Max-Sharpness and Min-Sharpness, aims to provide a more robust flatness indicator, leading to improved generalization and robustness across various tasks such as classification, transfer learning, and pose estimation. Theoretical analysis suggests BSAM converges to local minima, and experimental results show its superiority over standard SAM. AI

IMPACT Introduces a new optimization method that could improve neural network generalization and robustness.

RANK_REASON The cluster contains an academic paper detailing a new optimization technique for neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Flatness Indicator Problem
• Hugging Face
• Jiaxin Deng
• Max-Sharpness
• Min-Sharpness
• Sharpness Aware Minimization

• paper
• other

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