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 51–100 of 124 papers

TitleStatusHype
One-Shot Unsupervised Cross Domain TranslationCode0
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to NightCode0
Improving Shape Deformation in Unsupervised Image-to-Image TranslationCode0
One-Sided Unsupervised Domain MappingCode0
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image TranslationCode0
Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared DiscriminatorCode0
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial NetworksCode0
Unsupervised Image-to-Image Translation with Self-Attention NetworksCode0
Estimating the Success of Unsupervised Image to Image TranslationCode0
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous drivingCode0
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translationCode0
Demystifying Inter-Class DisentanglementCode0
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution AlignmentCode0
Unsupervised Video-to-Video TranslationCode0
MCMI: Multi-Cycle Image Translation with Mutual Information ConstraintsCode0
Wavelet-based Unsupervised Label-to-Image TranslationCode0
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksCode0
InstaGAN: Instance-aware Image-to-Image TranslationCode0
Unsupervised Attention-guided Image to Image TranslationCode0
Unsupervised Attention-guided Image-to-Image TranslationCode0
Medical Image Generation using Generative Adversarial Networks—0
Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples—0
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
Unsupervised Many-to-Many Image-to-Image Translation Across Multiple Domains—0
Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Contrastive Learning for Unsupervised Image-to-Image Translation—0
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation—0
Dense Multitask Learning to Reconfigure Comics—0
Disentangled Unsupervised Image Translation via Restricted Information Flow—0
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients—0
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency—0
Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation—0
Federated CycleGAN for Privacy-Preserving Image-to-Image Translation—0
Few-Shot Unsupervised Image-to-Image Translation on complex scenes—0
GAIT: Gradient Adjusted Unsupervised Image-to-Image Translation—0
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer—0
Image-to-Image Translation Based on Deep Generative Modeling for Radiotherapy Synthetic Dataset Creation—0
Improving Style-Content Disentanglement in Image-to-Image Translation—0
Invertible Autoencoder for domain adaptation—0
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