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
InstaGAN: Instance-aware Image-to-Image TranslationCode0
Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation—0
Unsupervised Attention-guided Image-to-Image TranslationCode0
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound—0
Refacing: reconstructing anonymized facial features using GANsCode0
Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis—0
Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching—0
Improving Shape Deformation in Unsupervised Image-to-Image TranslationCode0
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks—0
Specular-to-Diffuse Translation for Multi-View Reconstruction—0
One-Shot Unsupervised Cross Domain TranslationCode0
Unsupervised Video-to-Video TranslationCode0
Unsupervised Attention-guided Image to Image TranslationCode0
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency—0
XOGAN: One-to-Many Unsupervised Image-to-Image Translation—0
Invertible Autoencoder for domain adaptation—0
Estimating the Success of Unsupervised Image to Image TranslationCode0
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders—0
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial NetworksCode0
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many MappingsCode0
CyCADA: Cycle-Consistent Adversarial Domain AdaptationCode0
One-Sided Unsupervised Domain MappingCode0
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksCode0
Unsupervised Image-to-Image Translation with Generative Adversarial Networks—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