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
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 NetworkCode1
GAIT: Gradient Adjusted Unsupervised Image-to-Image Translation—0
Unsupervised MRI Reconstruction with Generative Adversarial NetworksCode1
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation—0
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation ApproachCode1
Toward Zero-Shot Unsupervised Image-to-Image TranslationCode1
The Surprising Effectiveness of Linear Unsupervised Image-to-Image TranslationCode1
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style EncoderCode2
Improving Style-Content Disentanglement in Image-to-Image Translation—0
MCMI: Multi-Cycle Image Translation with Mutual Information ConstraintsCode0
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer—0
Rethinking the Truly Unsupervised Image-to-Image TranslationCode1
Breaking the Cycle - Colleagues Are All You NeedCode1
DUNIT: Detection-Based Unsupervised Image-to-Image TranslationCode1
Medical Image Generation using Generative Adversarial Networks—0
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo MatchingCode1
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired ImagesCode1
Structural-analogy from a Single Image PairCode1
Feature Quantization Improves GAN TrainingCode1
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution AlignmentCode0
Lifespan Age Transformation SynthesisCode1
High-Resolution Daytime Translation Without Domain LabelsCode0
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image TranslationCode1
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image TranslationCode1
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation—0
Unsupervised Many-to-Many Image-to-Image Translation Across Multiple Domains—0
Breaking the cycle -- Colleagues are all you needCode1
Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation—0
Mocycle-GAN: Unpaired Video-to-Video Translation—0
Adversarial Self-Defense for Cycle-Consistent GANsCode0
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image TranslationCode1
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to NightCode0
Demystifying Inter-Class DisentanglementCode0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous drivingCode0
Joint haze image synthesis and dehazing with mmd-vae losses—0
Few-Shot Unsupervised Image-to-Image TranslationCode1
Unsupervised one-to-many image translation—0
Instance-aware Image-to-Image TranslationCode0
TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation—0
Semantics-Aware Image to Image Translation and Domain Transfer—0
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image TranslationCode0
Semi-Supervised Image-to-Image Translation—0
Unsupervised Image-to-Image Translation with Self-Attention NetworksCode0
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
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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