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 # gans-awesome-applications
-Curated list of awesome GAN applications and demo
+Curated list of awesome GAN applications and demonstrations.  
+__Note: General GAN papers targeting simple image generation such as DCGAN, BEGAN etc. are not included in the list.__
+
+## The landmark papers that I respect.
++ [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661), [[github]](https://github.com/goodfeli/adversarial)
++ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/pdf/1511.06434), [[github]](https://github.com/soumith/dcgan.torch)
++ [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/pdf/1703.10717), [[github]](https://github.com/carpedm20/BEGAN-tensorflow)
+
+-----
+
+## Applications using GANs
+
+### Font generation
++ [Learning Chinese Character style with conditional GAN](https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html), [[github]](https://github.com/kaonashi-tyc/zi2zi)
+
+### Anime character generation
++ [Towards the Automatic Anime Characters Creation with Generative Adversarial Networks](https://arxiv.org/pdf/1708.05509)
+
+### Interactive Image generation
++ [Generative Visual Manipulation on the Natural Image Manifold](https://arxiv.org/pdf/1609.03552), [[github]](https://github.com/junyanz/iGAN)
++ [Neural Photo Editing with Introspective Adversarial Networks](http://arxiv.org/abs/1609.07093), [[github]](https://github.com/ajbrock/Neural-Photo-Editor)
+
+### Text2Image (text to image)
++ [TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network](https://arxiv.org/pdf/1703.06412.pdf), [[github]](https://github.com/dashayushman/TAC-GAN)
++ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://arxiv.org/pdf/1612.03242.pdf), [[github]](https://github.com/hanzhanggit/StackGAN)
++ [Generative Adversarial Text to Image Synthesis](https://arxiv.org/pdf/1605.05396.pdf), [[github]](https://github.com/paarthneekhara/text-to-image)
+
+### 3D Obejct generation
++ Parametric 3D Exploration with Stacked Adversarial Networks, [[github]](https://github.com/maxorange/pix2vox), [[Youtube]](https://www.youtube.com/watch?v=ITATOXVvWEM)
++ [Learning a Probabilistic Latent Space of Object
+Shapes via 3D Generative-Adversarial Modeling](http://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling.pdf), [[github]](https://github.com/zck119/3dgan-release), [[Youtube]](https://www.youtube.com/watch?v=HO1LYJb818Q)
+
+### Photorealistic Image geneation (e.g. pix2pix, sketch2image)
++ [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004), [[github]](https://github.com/phillipi/pix2pix), [[Youtube]](https://www.youtube.com/watch?v=VVqxbmUJorQ)
+
+### Domain-transfer (e.g. style-transfer)
++ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593.pdf), [[github]](https://github.com/junyanz/CycleGAN), [[Youtube]](https://www.youtube.com/watch?v=JzgOfISLNjk)
++ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://arxiv.org/pdf/1703.05192.pdf), [[github]](https://github.com/carpedm20/DiscoGAN-pytorch)
++ [Unsupervised Creation of Parameterized Avatars](https://arxiv.org/pdf/1704.05693.pdf)
++ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION](https://openreview.net/pdf?id=Sk2Im59ex)
+
+### Image Inpainting (hole filling)
++ [Context Encoders: Feature Learning by Inpainting](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf), [[github]](https://github.com/pathak22/context-encoder)
+
+-----
+
+## Did not use GAN, but still interesting applications.
+
+### Real-time face reconstruction
++ [Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction](https://arxiv.org/pdf/1703.10580.pdf), [[github]](), [[Youtube]](https://www.youtube.com/watch?v=uIMpHZYB8fI)
+
+### Super-resolution
++ [Learning to Simplify:
+Fully Convolutional Networks for Rough Sketch Cleanup](http://delivery.acm.org/10.1145/2930000/2925972/a121-simo-serra.pdf?ip=111.91.137.238&id=2925972&acc=ACTIVE%20SERVICE&key=58C7DD92F91E3631%2E58C7DD92F91E3631%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=818332500&CFTOKEN=94661101&__acm__=1507786813_0e5b28dfb97e654d0126d61b0aa592f4), [[site link]](http://hi.cs.waseda.ac.jp/~esimo/en/research/sketch/), [[Youtube]](https://www.youtube.com/watch?v=4MfG9CDufPA)
+
+### Photorealistic Image geneation (e.g. pix2pix, sketch2image)
++ [The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies](http://delivery.acm.org/10.1145/2930000/2925954/a119-sangkloy.pdf?ip=111.91.137.238&id=2925954&acc=CHORUS&key=58C7DD92F91E3631%2E58C7DD92F91E3631%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=818332500&CFTOKEN=94661101&__acm__=1507787415_cb950c300370fc27da68920a0d5b5178), [[Youtube]](https://www.youtube.com/watch?v=a3sgFQjEfp4)
++ [PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing](https://www.researchgate.net/profile/Eli_Shechtman/publication/220184392_PatchMatch_A_Randomized_Correspondence_Algorithm_for_Structural_Image_Editing/links/02e7e520897b12bf0f000000.pdf), [[github]](https://github.com/younesse-cv/PatchMatch), [[Youtube]](https://www.youtube.com/watch?v=n3aoc36V8LM)
+
+
+-----
+
+## GANs tutorials with easy and simple example codes for starters.
++ [1D Generative Adversarial Network Demo](http://notebooks.aylien.com/research/gan/gan_simple.html)
++ [](), [[github]](), [[Youtube]]()
+
+## Author
+Minchul Shin, [@nashory](https://github.com/nashory)