常見GAN

最後，作為 GAN 專題的結尾，我們列舉一下目前常見的 GAN 模型（可以根據 arxiv id 去尋找、下載文獻），歡迎補充。

GAN - Ian Goodfellow, arXiv:1406.2661v1

DCGAN - Alec Radford & Luke Metz, arxiv:1511.06434

CGAN - Mehdi Mirza, arXiv:1411.1784v1

LAPGAN - Emily Denton & Soumith Chintala, arxiv: 1506.05751

InfoGAN - Xi Chen, arxiv: 1606.03657

PPGAN - Anh Nguyen, arXiv:1612.00005v1

WGAN - Martin Arjovsky, arXiv:1701.07875v1

LS-GAN - Guo-Jun Qi, arxiv: 1701.06264

SeqGAN - Lantao Yu, arxiv: 1609.05473

EBGAN - Junbo Zhao, arXiv:1609.03126v2

VAEGAN - Anders Boesen Lindbo Larsen, arxiv: 1512.09300

......

此外，還有一些在特定任務中提出來的模型，如本期介紹的 GAN-CLS、GAN-INT、SRGAN、iGAN、IAN 等等，這裡就不再列舉。

程式碼

LS-GAN

Torch 版本：https://github.com/guojunq/lsgan

SRGAN

TensorFlow 版本：https://github.com/buriburisuri/SRGAN

Torch 版本：https://github.com/leehomyc/Photo-Realistic-Super-Resoluton

Keras 版本：https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks

iGAN

Theano 版本：https://github.com/junyanz/iGAN

IAN

Theano 版本：https://github.com/ajbrock/Neural-Photo-Editor

Pix2pix

Torch 版本：https://github.com/phillipi/pix2pix

TensorFlow 版本：https://github.com/yenchenlin/pix2pix-tensorflow

GAN for Neural dialogue generation

Torch 版本：https://github.com/jiweil/Neural-Dialogue-Generation

Text2image

Torch 版本：https://github.com/reedscot/icml2016

TensorFlow+Theano 版本：https://github.com/paarthneekhara/text-to-image

GAN for Imitation Learning

Theano 版本：https://github.com/openai/imitation

SeqGAN

TensorFlow 版本：https://github.com/LantaoYu/SeqGAN

參考文獻

Qi G J. Loss-Sensitive Generative Adversarial Networks onLipschitz Densities[J]. arXiv preprint arXiv:1701.06264, 2017.

Li J, Monroe W, Shi T, et al. Adversarial Learning for NeuralDialogue Generation[J]. arXiv preprint arXiv:1701.06547, 2017.

Sønderby C K, Caballero J, Theis L, et al. Amortised MAPInference for Image Super-resolution[J]. arXiv preprint arXiv:1610.04490, 2016.

Ravanbakhsh S, Lanusse F, Mandelbaum R, et al. Enabling DarkEnergy Science with Deep Generative Models of Galaxy Images[J]. arXiv preprintarXiv:1609.05796, 2016.

Ho J, Ermon S. Generative adversarial imitationlearning[C]//Advances in Neural Information Processing Systems. 2016:4565-4573.

Zhu J Y, Krähenbühl P, Shechtman E, et al. Generative visualmanipulation on the natural image manifold[C]//European Conference on ComputerVision. Springer International Publishing, 2016: 597-613.

Isola P, Zhu J Y, Zhou T, et al. Image-to-image translationwith conditional adversarial networks[J]. arXiv preprint arXiv:1611.07004,2016.

Shrivastava A, Pfister T, Tuzel O, et al. Learning fromSimulated and Unsupervised Images through Adversarial Training[J]. arXivpreprint arXiv:1612.07828, 2016.

Ledig C, Theis L, Huszár F, et al. Photo-realistic singleimage super-resolution using a generative adversarial network[J]. arXivpreprint arXiv:1609.04802, 2016.

Nguyen A, Yosinski J, Bengio Y, et al. Plug & playgenerative networks: Conditional iterative generation of images in latentspace[J]. arXiv preprint arXiv:1612.00005, 2016.

Yu L, Zhang W, Wang J, et al. Seqgan: sequence generativeadversarial nets with policy gradient[J]. arXiv preprint arXiv:1609.05473,2016.

Lotter W, Kreiman G, Cox D. Unsupervised learning of visualstructure using predictive generative networks[J]. arXiv preprintarXiv:1511.06380, 2015.

Reed S, Akata Z, Yan X, et al. Generative adversarial textto image synthesis[C]//Proceedings of The 33rd International Conference onMachine Learning. 2016, 3.

Brock A, Lim T, Ritchie J M, et al. Neural photo editingwith introspective adversarial networks[J]. arXiv preprint arXiv:1609.07093,2016.

Pfau D, Vinyals O. Connecting generative adversarialnetworks and actor-critic methods[J]. arXiv preprint arXiv:1610.01945, 2016.