XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity

ArXi:2605.09639v1 Announce Type: cross While U-Net architectures remain the gold standard for medical image segmentation, their deployment in resource-constrained environments demands aggressive model compression. However, finding an optimally efficient configuration is computationally prohibitive, typically requiring exhaustive train-and-evaluate cycles to find the smallest model that maintains peak performance. In this paper, we