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

Showing 51–60 of 124 papers

TitleStatusHype
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain ClassificationCode0
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style TranslationCode0
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution—0
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation—0
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators—0
Unsupervised Image to Image Translation for Multiple Retinal Pathology Synthesis in Optical Coherence Tomography Scans—0
Disentangled Unsupervised Image Translation via Restricted Information Flow—0
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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