arXiv:2607.06151v1 Announce Type: new Abstract: 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 inv
The continuous drive for improved generalization in deep learning models necessitates novel optimization techniques beyond traditional methods, addressing persistent challenges like overfitting to sharp minima.
Improved optimization algorithms directly translate to more robust, efficient, and reliable AI models, impacting the performance and utility of AI across various applications and accelerating AI development.
The introduction of EISAM, building on SAM with an extragradient technique, offers a new pathway to achieve flatter minima and better generalization in deep learning compared to prevailing methods.
- · Deep Learning researchers
- · AI model developers
- · Companies deploying AI
- · Industries relying on AI generalization
- · Platforms with suboptimal optimization processes
This research provides a more effective method for training deep learning models that generalize better to unseen data.
Enhanced model generalization could lead to more reliable AI systems in critical applications and a reduction in the computational resources needed for extensive data collection.
The widespread adoption of such advanced optimization techniques could accelerate the development of more complex and autonomous AI agents, pushing the boundaries of AI capabilities.
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Read at arXiv cs.LG