Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Paper summary _Objective:_ Propose a more stable set of architectures for training GAN and show that they learn good representations of images for supervised learning and generative modeling. _Dataset:_ [LSUN](http://www.yf.io/p/lsun) and [ImageNet 1k](www.image-net.org/). ## Architecture: Below are the guidelines for making DCGANs. [![screen shot 2017-04-24 at 10 58 17 am](https://cloud.githubusercontent.com/assets/17261080/25329644/f3885f7c-28dc-11e7-8895-051124c8ff6c.png)](https://cloud.githubusercontent.com/assets/17261080/25329644/f3885f7c-28dc-11e7-8895-051124c8ff6c.png) And here is a sample network: [![screen shot 2017-04-24 at 10 57 54 am](https://cloud.githubusercontent.com/assets/17261080/25329634/e9c14abc-28dc-11e7-8bed-068f7f7bc78d.png)](https://cloud.githubusercontent.com/assets/17261080/25329634/e9c14abc-28dc-11e7-8bed-068f7f7bc78d.png) A tensorflow implementation can be found [here](https://github.com/carpedm20/DCGAN-tensorflow) along with an [online demo](https://carpedm20.github.io/faces/). ## Results: Quite interesting especially concerning the structure learned in the Z-space and how this can be used for interpolation or object removal, see the example that is shown everywhere: [![screen shot 2017-04-24 at 11 20 03 am](https://cloud.githubusercontent.com/assets/17261080/25330458/080b6b4e-28e0-11e7-9ab6-ce58ef5b5562.png)](https://cloud.githubusercontent.com/assets/17261080/25330458/080b6b4e-28e0-11e7-9ab6-ce58ef5b5562.png) Nonetheless the network is still generating small images (32x32).
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford and Luke Metz and Soumith Chintala
arXiv e-Print archive - 2015 via Local arXiv
Keywords: cs.LG, cs.CV

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