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

# Unsupervised Image-To-Image Translation Edit

19 papers with code · 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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# Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation

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.

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

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.

# 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).

# 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.

# Unsupervised Video-to-Video Translation

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 one-to-many image translation

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).

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

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.

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

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.

# Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

28 Mar 2019

To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.

# 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.