Image-to-Image Translation with Conditional Adversarial Networks

@article{Isola2017ImagetoImageTW,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Phillip Isola and Jun-Yan Zhu and Tinghui Zhou and Alexei A. Efros},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={5967-5976}
}
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. [] Key Result As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

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Image-to-Image Translation Using Generative Adversarial Network

  • Kusam LataM. DaveK. Nishanth
  • Computer Science
    2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA)
  • 2019
Conditional GANs are used which translates the images based upon some conditions and the performance is also analyzed of the model by doing hyper-parameter tuning.

Unpaired Image-to-Image Translation using Adversarial Consistency Loss

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In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

This paper introduces a Generative Adversarial Network (GAN) based framework along with a multi-modal generator structure and a new loss term, latent consistency loss, and shows that leveraging multiple inputs generally improves the visual quality of the translated images.

Equivariant Adversarial Network for Image-to-image Translation

A trainable transformer is used, which explicitly allows the spatial manipulation of data within training, and this differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation.

An Input-Perceptual Reconstruction Adversarial Network for Paired Image-to-Image Conversion

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