Weight Dynamic

Weight dynamics in neural networks investigates how network weights change during training, aiming to understand and improve learning processes and generalization. Current research focuses on analyzing weight evolution in various architectures, including diffusion models and transformers, employing techniques like quantization, normalization, and weight inflation to optimize training efficiency and performance. Understanding weight dynamics is crucial for enhancing model trainability, reducing computational costs, and improving generalization capabilities across diverse applications, such as image generation and medical image segmentation. These advancements contribute to building more efficient and effective deep learning models.