Computer Vision
 
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Image-to-Image Translation

491 papers with code • 37 benchmarks • 29 datasets

Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.

( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )

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Use these libraries to find Image-to-Image Translation models and implementations

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Most implemented papers

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Domain Adaptation for Structured Output via Discriminative Patch Representations

wasidennis/AdaptSegNet • • ICCV 2019

Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.

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

Ha0Tang/AttentionGAN • • 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.

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GANimation: Anatomically-aware Facial Animation from a Single Image

albertpumarola/GANimation • • ECCV 2018

Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.

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Diverse Image-to-Image Translation via Disentangled Representations

HsinYingLee/DRIT • • ECCV 2018

Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.

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Unsupervised Cross-Domain Image Generation

kaonashi-tyc/zi2zi • • 7 Nov 2016

We study the problem of transferring a sample in one domain to an analog sample in another domain.

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Invertible Conditional GANs for image editing

Guim3/IcGAN • • 19 Nov 2016

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.

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DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

duxingren14/DualGAN • • ICCV 2017

Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.

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Virtual to Real Reinforcement Learning for Autonomous Driving

rahul263-stack/Self-Driving-Car • • 13 Apr 2017

To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.

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Toward Multimodal Image-to-Image Translation

junyanz/BicycleGAN • • NeurIPS 2017

Our proposed method encourages bijective consistency between the latent encoding and output modes.

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CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

mnicnc404/CartoonGan-tensorflow • • CVPR 2018

Two novel losses suitable for cartoonization are proposed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network to cope with substantial style variation between photos and cartoons, and (2) an edge-promoting adversarial loss for preserving clear edges.

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