Flat Minimum

Flat minima, regions in a neural network's parameter space where the loss function changes slowly, are a focus of current research aiming to improve model generalization and robustness. Research investigates various optimization algorithms, including Sharpness-Aware Minimization (SAM) and its variants, momentum-based methods, and weight averaging techniques, to effectively locate these flatter regions within the often complex loss landscapes of deep learning models, such as convolutional and transformer networks. This research is significant because finding flat minima is empirically linked to better generalization performance and increased resistance to adversarial attacks, impacting both the theoretical understanding of deep learning and the practical development of more reliable and robust AI systems.