A Survey of Generative Adversarial Networks

Generative adversarial networks(GANs) coming from the game theory allow machines to learn deep representations without extra training data. By training two adversarial networks, including a generator and a discriminator, GANs could get the distribution of the real samples. This capability makes it a prospect learning method in image synthesis, image recognition, image translation etc. In this paper, we survey the state of the art of GANs by categorizing the GANs into four classifications on the basis of GANs' functions and list two application domains: vision computing & natural language processing(NLP) regarding to GANs' applications.

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[103]  Dacheng Tao,et al.  Perceptual Adversarial Networks for Image-to-Image Transformation , 2017, IEEE Transactions on Image Processing.

[104]  Sandeep Subramanian,et al.  Adversarial Generation of Natural Language , 2017, Rep4NLP@ACL.

[105]  Eric P. Xing,et al.  Dual Motion GAN for Future-Flow Embedded Video Prediction , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[106]  Ersin Yumer,et al.  Neural Face Editing with Intrinsic Image Disentangling , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[107]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

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[114]  Kunio Kashino,et al.  Generative Attribute Controller with Conditional Filtered Generative Adversarial Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[115]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[116]  Jan Kautz,et al.  MoCoGAN: Decomposing Motion and Content for Video Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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[118]  Lawrence Carin,et al.  ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching , 2017, NIPS.

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[120]  Yoshua Bengio,et al.  Maximum-Likelihood Augmented Discrete Generative Adversarial Networks , 2017, ArXiv.