Browse > Computer Vision > Image-to-Image Translation > Unsupervised Image-To-Image Translation

Unsupervised Image-To-Image Translation

22 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.

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Latest papers without code

Adversarial Self-Defense for Cycle-Consistent GANs

5 Aug 2019

The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains.

ADVERSARIAL ATTACK UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation

CVPR 2019

In multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain.

MULTIMODAL UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

TransGaGa: Geometry-Aware Unsupervised Image-To-Image Translation

CVPR 2019

Extensive experiments demonstrate the superior performance of our method to other state-of-the-art approaches, especially in the challenging near-rigid and non-rigid objects translation tasks.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

31 May 2019

In this context, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting images with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).

DATA AUGMENTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Joint haze image synthesis and dehazing with mmd-vae losses

15 May 2019

Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs.

AUTONOMOUS DRIVING IMAGE DEHAZING UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Unsupervised Video-to-Video Translation

ICLR 2019

Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Unsupervised one-to-many image translation

ICLR 2019

We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e. g., class, size, shape, etc).

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

ICLR 2019

Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation

CVPR 2019

Extensive experiments demonstrate the superior performance of our method to other state-of-the-art approaches, especially in the challenging near-rigid and non-rigid objects translation tasks.

UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Unsupervised Image-to-Image Translation with Self-Attention Networks

24 Jan 2019

Unsupervised image translation aims to learn the transformation from a source domain to another target domain given unpaired training data.

STYLE TRANSFER UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION