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Unsupervised Image-To-Image Translation

36 papers with code ยท Computer Vision

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 )

Benchmarks

Latest papers without code

Toward Zero-Shot Unsupervised Image-to-Image Translation

28 Jul 2020

Recent studies have shown remarkable success in unsupervised image-to-image translation.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION ZERO-SHOT LEARNING

COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder

15 Jul 2020

Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Improving Style-Content Disentanglement in Image-to-Image Translation

9 Jul 2020

Unsupervised image-to-image translation methods have achieved tremendous success in recent years.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

MCMI: Multi-Cycle Image Translation with Mutual Information Constraints

6 Jul 2020

We present a mutual information-based framework for unsupervised image-to-image translation.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer

23 Jun 2020

Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

DUNIT: Detection-Based Unsupervised Image-to-Image Translation

CVPR 2020

As evidenced by our experiments, this allows us to outperform the state-of-the-art unsupervised image-to-image translation methods.

OBJECT DETECTION UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

CVPR 2020

The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Medical Image Generation using Generative Adversarial Networks

19 May 2020

Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data.

IMAGE AUGMENTATION IMAGE RECONSTRUCTION IMAGE REGISTRATION MEDICAL IMAGE GENERATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION