arXiv:2205.15269
공식 코드 · Papers with Code
Kernel Neural Optimal Transport
원문 초록
We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost might learn fake plans which are not optimal. To resolve this issue, we introduce kernel weak quadratic costs. We show that they provide improved theoretical guarantees and practical performance. We test NOT with kernel costs on the unpaired image-to-image translation task.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
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
토론
이 논문과 연결된 포럼 글이 아직 없습니다.