REVIEW 7 cited by
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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
abstract
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping $F: Y \rightarrow X$ and introduce a cycle consistency loss to push $F(G(X)) \approx X$ (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Forward citations
Cited by 7 Pith papers
-
Bridging Scales in Map Generation: A scale-aware cascaded generative mapping framework for seamless and consistent multi-scale cartographic representation
A cascaded latent-diffusion framework with CLIP-based scale encoding and cascade references generates seamless multi-scale tile maps from remote sensing imagery, reporting state-of-the-art FID/PSNR on MLMG and CSCMG b...
-
Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting
A modified CSDI diffusion model with convolutions, RMSNorm, and Fourier encoding jointly imputes and forecasts hydrological time series, outperforming standard baselines on two datasets for short horizons.
-
Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification
A CycleGAN-based counterfactual framework translates diseased retinal images to healthy-looking counterparts, and a new CCAS metric scores spatial agreement between the translation difference maps and classifier saliency.
-
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models
Fine-tuning Depth Anything V2 on physics-based synthetic underwater versions of Hypersim improves metric depth accuracy on real underwater benchmarks like FLSea and SQUID, though one AbsRel number worsens slightly.
-
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis
A video diffusion model, HunyuanVideo-I2V, is adapted with mixup transitions, frame-skip position embeddings, and attention masking to outperform image-only models on several controllable image generation benchmarks.
-
SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant Surgical Scene Completion
A single-step denoising diffusion GAN with a Patch-GAN discriminator completes surgical microscope scenes, reporting higher SSIM than several inpainting baselines on a small single-patient dataset.
-
Learning Text Styles: A Study on Transfer, Attribution, and Verification
A thesis compiles published work claiming that lightweight adapters, contrastive disentanglement, and instruction tuning improve text style transfer, authorship attribution, and authorship verification.
Discussion (0). Continue with ORCID to comment.