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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.__ | ||
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## 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) | ||
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## Applications using GANs | ||
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### 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) | ||
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### Anime character generation | ||
+ [Towards the Automatic Anime Characters Creation with Generative Adversarial Networks](https://arxiv.org/pdf/1708.05509) | ||
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### 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) | ||
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### 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) | ||
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### 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) | ||
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### 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) | ||
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### 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) | ||
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### 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) | ||
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## Did not use GAN, but still interesting applications. | ||
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### 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) | ||
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### 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) | ||
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### 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) | ||
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## 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]]() | ||
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## Author | ||
Minchul Shin, [@nashory](https://github.com/nashory) |