Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
arXiv:2507.18112v1 Announce Type: cross Abstract: We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance imaging (MRI) image generation. Despite its practical significance, research on parameter-efficient representations of 3D convolution operations remains limited. To bridge this gap, we propose Tensor Volumetric Operator (TenVOO), a novel […]