DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights

ArXi:2601.05052v2 Announce Type: replace Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenges posed by the high-dimensional weight spaces of modern neural networks and their symmetries. Several prior generative models are limited to generating partial neural network weights, particularly for larger models, such as ResNet and ViT. Those that do generate complete weights struggle with generation speed or require finetuning of the generated models.