EMASAM offers efficient, stable alternative to SAM for model generalization

Researchers have introduced EMASAM, a new optimization technique designed to improve model generalization while reducing computational cost. Unlike traditional Sharpness-Aware Minimization (SAM), EMASAM bypasses the need for an extra gradient computation during its perturbation step. It achieves this by using the discrepancy between a main model and its exponential moving average (EMA) shadow model to guide perturbations, offering a more stable and efficient alternative. AI

IMPACT EMASAM could reduce training costs and improve the generalization capabilities of machine learning models.

RANK_REASON The cluster contains a research paper detailing a new optimization method for machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• DagsHub
• EMASAM
• exponential moving average
• Hugging Face
• SAM
• Sharpness-Aware Minimization
• Tanapat Ratchatorn

• paper
• other

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