Unpaired Image-to-Image Translation via Neural Schr"odinger Bridge
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved remarkable progress, they have limitations in unpaired image-to-image (I2I) translation tasks due to the Gaussian prior assumption. Schr"odinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. Yet, to our best knowledge, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose Unpaired Neural Schr"odinger Bridge (UNSB), which expresses the SB problem as a sequence of adversarial learning problems. This allows us to incorporate advanced discriminators and regularization to learn a SB between unpaired data. We show that UNSB is scalable and successfully solves various unpaired I2I translation tasks. Code: urlhttps://github.com/cyclomon/UNSB
Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 44 of its 57 references that are in the map. First posted on arXiv 24 May 2023.
Builds on
44 of 57 references.
Closest ancestors · 11
Same direction, newest first
- 2023Diffusion Schr"odinger Bridge Matching29 Mar
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- 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
- 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANs15 Dec
- 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modeling1 Jun
- 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
- 2020Denoising Diffusion Implicit Models6 Oct
- 2020Denoising Diffusion Probabilistic Models19 Jun
Other references · 33
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
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- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
- 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
- 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
- 2014NICE: Non-linear Independent Components Estimationcited by 1,052
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2018MINE: Mutual Information Neural Estimationcited by 680
- 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 637
- 2013A survey of the Schr"odinger problem and some of its connections with optimal transportcited by 286
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- 2021Likelihood Training of Schr"odinger Bridge using Forward-Backward SDEs Theorycited by 216
- 2022Dual Diffusion Implicit Bridges for Image-to-Image Translationcited by 212
- 2021Path Integral Sampler: a stochastic control approach for samplingcited by 175
- 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 164
- 2021Solving Schr"odinger Bridges via Maximum Likelihoodcited by 140
- 2022Neural Optimal Transportcited by 123
- 2018Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mappingcited by 113
- 2021Deep Generative Learning via Schr"odinger Bridgecited by 103
- 2017One-Sided Unsupervised Domain Mappingcited by 91
- 2021The Spatially-Correlative Loss for Various Image Translation Taskscited by 66
- 2022The Schr"odinger Bridge between Gaussian Measures has a Closed Formcited by 63
- 2020Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translationcited by 60
- 2022Entropic Neural Optimal Transport via Diffusion Processescited by 49
- 2022Deep Generalized Schr"odinger Bridgecited by 47
- 2021Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translationcited by 45
- 2022Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Taskscited by 42
- 2022Can Push-forward Generative Models Fit Multimodal Distributions?cited by 41
- 2020Reflected Schr"odinger Bridge: Density Control with Path Constraintscited by 22
- 2023Transport with Support: Data-Conditional Diffusion Bridgescited by 9
- 2022Unbiased Estimation using a Class of Diffusion Processescited by 4
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