Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data. Building on Sharpness-Aware Minimization (SAM), for seeking flat minima associated with improved generalization, we propose the Extragradient-Inspired Sharpness-Aware Minimization (EISAM), a novel optimizer that enhances generalization via the extragradient technique. EISAM uses a two-step update process: a prediction step investigating the geometry of the loss landscape an
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- PossiblePossibly related (embedding) · 45%Multi-modal deep learning model for visual acuity prediction from wide field colour fundus imaging - Nature →
- LinkedLinked via arxiv author · 85%Yao Fu →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”
- LinkedLinked via arxiv author · 85%Chunxia Zhang →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”
- LinkedLinked via arxiv author · 85%Junmin Liu →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”
- LinkedLinked via arxiv author · 85%Yihang Jin →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”
- LinkedLinked via arxiv author · 85%Haishan Ye →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”
- LinkedLinked via arxiv author · 85%Yuanao Yang →
“Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning”