SOTAVerified

Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation is the task of doing image-to-image translation without ground truth image-to-image pairings.

( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–124 of 124 papers

TitleStatusHype
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution—0
Multi-Platform Methane Plume Detection via Model and Domain Adaptation—0
Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint—0
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation—0
Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images—0
Semantics-Aware Image to Image Translation and Domain Transfer—0
Semi-Supervised Image-to-Image Translation—0
Six-channel Image Representation for Cross-domain Object Detection—0
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation—0
Specular-to-Diffuse Translation for Multi-View Reconstruction—0
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology—0
TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation—0
Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review—0
Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis—0
Unsupervised Image to Image Translation for Multiple Retinal Pathology Synthesis in Optical Coherence Tomography Scans—0
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound—0
Unsupervised Image-to-Image Translation with Generative Adversarial Networks—0
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks—0
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders—0
Unsupervised one-to-many image translation—0
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables—0
XOGAN: One-to-Many Unsupervised Image-to-Image Translation—0
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All datasetsSVNH-to-MNISTFreiburg Forest Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CyCADA pixel+featClassification Accuracy90.4—Unverified
2DTNClassification Accuracy84.4—Unverified
3ADDAClassification Accuracy76—Unverified
4DANNClassification Accuracy73.6—Unverified
#ModelMetricClaimedVerifiedStatus
1In2IPSNR21.65—Unverified
2cycGANPSNR18.57—Unverified
3UNITPSNR9.42—Unverified