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Image-to-Image Translation with Conditional Adversarial Networks

Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

This work presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples, and introduces a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
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Semantic Image Synthesis With Spatially-Adaptive Normalization

S spatially-adaptive normalization is proposed, a simple but effective layer for synthesizing photorealistic images given an input semantic layout that allows users to easily control the style and content of image synthesis results as well as create multi-modal results.
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CyCADA: Cycle-Consistent Adversarial Domain Adaptation

A novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model that adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs is proposed.
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Toward Multimodal Image-to-Image Translation

This work aims to model a distribution of possible outputs in a conditional generative modeling setting that helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse.
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Contrastive Learning for Unpaired Image-to-Image Translation

The framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time, and can be extended to the training setting where each "domain" is only a single image.
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Video-to-Video Synthesis

This paper proposes a novel video-to-video synthesis approach under the generative adversarial learning framework, capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis.
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Generative Visual Manipulation on the Natural Image Manifold

This paper proposes to learn the natural image manifold directly from data using a generative adversarial neural network, and defines a class of image editing operations, and constrain their output to lie on that learned manifold at all times.
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Generating Adversarial Examples with Adversarial Networks

Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks, and have placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.
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