{"published":"2018-04-15 00:14:06","title":"Tutorial for DCGAN with Chainer","version":"1.0","height":"190","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fkumechann.hatenablog.com%2Fentry%2F2018%2F04%2F15%2F001406\" title=\"Tutorial for DCGAN with Chainer - \u91d1\u878d\u3068\u5de5\u5b66\u306e\u3042\u3044\u3060\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","provider_name":"Hatena Blog","url":"https://kumechann.hatenablog.com/entry/2018/04/15/001406","width":"100%","description":"DCGAN: Generate the images with Deep Convolutional GAN 0. Introduction In this tutorial, we generate images with generative adversarial network (GAN). It is a kind of generative model with deep neural network, and often applied to the image generation. The GAN technique is also applied to PaintsChai\u2026","provider_url":"https://hatena.blog","author_url":"https://blog.hatena.ne.jp/kumechann/","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/k/kumechann/20180414/20180414233532.gif","blog_url":"https://kumechann.hatenablog.com/","categories":[],"type":"rich","author_name":"kumechann","blog_title":"\u91d1\u878d\u3068\u5de5\u5b66\u306e\u3042\u3044\u3060"}