arXiv:2201.12220
공식 코드 · Papers with Code
Neural Optimal Transport
원문 초록
We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image translation.
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CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Cora: Correspondence-aware image editing using few step diffusion
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Paired and Unpaired Image to Image Translation using Generative Adversarial Networks
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
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