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+__[Slides]__ - [here](https://yadi.sk/i/FfYNOafFKxPevA)
+
+### Materials
+* Russian: [Generative adversarial networks](https://yadi.sk/i/I1WBh0TM3GUoky), [Advanced theory on GANs](https://yadi.sk/i/Gi-8h2Ph3T87ep)
+* English: [Generative adversarial networks](https://www.youtube.com/watch?v=HN9NRhm9waY)
+
+### More materials
+* Pix2Pix online demo (conditional adversarial networks) - [pix2pix](https://affinelayer.com/pixsrv/)
+* Articles: [Original](https://arxiv.org/abs/1406.2661), [Energy-Based](https://arxiv.org/abs/1609.03126), [Least Squares](https://arxiv.org/abs/1611.04076), [Wasserstein](https://arxiv.org/abs/1701.07875), etc.
+* GANs with variational aftertaste - [alpha-GAN](https://arxiv.org/abs/1706.04987) (and others, just google)
+* Domain adaptation with adversarial networks - [arxiv](https://arxiv.org/abs/1409.7495)
+* Unsupervised relation learning with CycleGan - [arxiv](https://arxiv.org/abs/1703.10593)
+* Word translation without parallel data - [arxiv](https://arxiv.org/pdf/1710.04087.pdf), [github](https://github.com/facebookresearch/MUSE)
+* Srsly, there's a lot of cool stuff about GANs. Just google it.
+