REVIEW 5 cited by
Dual Diffusion Implicit Bridges for Image-to-Image Translation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Common image-to-image translation methods rely on joint training over data from both source and target domains. The training process requires concurrent access to both datasets, which hinders data separation and privacy protection; and existing models cannot be easily adapted for translation of new domain pairs. We present Dual Diffusion Implicit Bridges (DDIBs), an image translation method based on diffusion models, that circumvents training on domain pairs. Image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images. Both steps are defined via ordinary differential equations (ODEs), thus the process is cycle consistent only up to discretization errors of the ODE solvers. Theoretically, we interpret DDIBs as concatenation of source to latent, and latent to target Schrodinger Bridges, a form of entropy-regularized optimal transport, to explain the efficacy of the method. Experimentally, we apply DDIBs on synthetic and high-resolution image datasets, to demonstrate their utility in a wide variety of translation tasks and their inherent optimal transport properties.
Forward citations
Cited by 5 Pith papers
-
DualMat: PBR Material Estimation via Coherent Dual-Path Diffusion
DualMat is a dual-path diffusion model combining an albedo-optimized pretrained latent path with a material-specialized compact latent path, using feature distillation and rectified flow to estimate PBR materials from...
-
Score-based Diffusion Model for Unpaired Virtual Histology Staining
An unpaired, mutual-information-guided diffusion model translates H&E histology images into IHC images with improved structural and staining fidelity.
-
Translationese as a Rational Response to Translation Task Difficulty
Translationese is partly predictable from quantifiable translation-task difficulty, especially cross-lingual transfer load, more so for English-to-German than the reverse.
-
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
CycleVAR adapts a pretrained visual autoregressive model to unpaired image translation using softmax-relaxed quantization and source-token prefixes, achieving FID scores competitive with CycleGAN-Turbo.
-
The Principles of Diffusion Models
A principled monograph showing that variational, score-based, and flow-based diffusion models are instances of one continuous-time transport backbone, with sampling equal to solving a differential equation governed by...
Discussion (0). Sign in to comment.