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 91–100 of 124 papers

TitleStatusHype
Joint haze image synthesis and dehazing with mmd-vae losses—0
Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation—0
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation—0
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling—0
Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching—0
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators—0
Memory-guided Unsupervised Image-to-image Translation—0
Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation—0
Mocycle-GAN: Unpaired Video-to-Video Translation—0
Multi-cropping Contrastive Learning and Domain Consistency for 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