{
  "id": 97792,
  "title": "Getting started: reading material",
  "url": "/competitions/generative-dog-images/discussion/97792",
  "author_name": "",
  "post_date": "2019-06-28T22:09:26.169066400Z",
  "votes": 36,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Here is some reading material that might be relevant for this competition. Please add/share anything that might be useful or relevant :)</p>\n\n<h3>Resources for Generative Networks</h3>\n\n<ul>\n<li><a href=\"http://cs229.stanford.edu/notes/cs229-notes2.pdf\">“Generative Learning algorithms” - Andrew Ng’s Stanford notes</a></li>\n<li><a href=\"http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf\">On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, by Andrew Ng and Michael I. Jordan</a></li>\n<li><a href=\"https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html\">The Math Behind Generative Adversarial Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/1812.04948\">A Style-Based Generator Architecture for Generative Adversarial Networks, Code – Examples of StyleGAN in action: Faces, Anime, Art – Description of the StyleGAN architecture</a></li>\n<li><a href=\"https://github.com/hamaadshah/gan_deeplearning4j\">Automatic feature engineering using Generative Adversarial Networks with Deeplearning4j &amp; Spark</a></li>\n<li><a href=\"https://arxiv.org/pdf/1906.00446.pdf\">Generating Diverse High-Fidelity Images with VQ-VAE-2</a></li>\n</ul>\n\n<h3>GAN Use Cases</h3>\n\n<ul>\n<li><a href=\"https://arxiv.org/abs/1605.05396\">Text to Image Generation</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.07004\">Image to Image Translation</a></li>\n<li><a href=\"https://arxiv.org/abs/1609.04802\">Increasing Image Resolution</a></li>\n<li><a href=\"https://arxiv.org/abs/1511.06380\">Predicting Next Video Frame</a></li>\n</ul>\n\n<h3>Notable Papers on GANs</h3>\n\n<ul>\n<li><strong>Generative Adversarial Nets</strong> <a href=\"https://arxiv.org/abs/1406.2661\">Paper</a> <a href=\"https://github.com/goodfeli/adversarial\">Code</a>(Ian Goodfellow’s breakthrough paper)</li>\n<li><a href=\"https://github.com/soumith/ganhacks\">GAN Hacks: How to Train a GAN? Tips and tricks to make GANs work</a></li>\n</ul>\n\n<h3>Tutorials</h3>\n\n<ul>\n<li><a href=\"https://sites.google.com/view/iccv-2017-gans/schedule\">ICCV 2017 Tutorial About GANS</a></li>\n</ul>\n\n<p><br></p>\n\n<hr>\n\n<p><strong>Source: <a href=\"https://skymind.ai/wiki/generative-adversarial-network-gan\">A Beginner's Guide to Generative Adversarial Networks (GANs)</a></strong></p>",
  "messages": [
    {
      "id": "564016",
      "postDate": "06/28/2019 22:09:26",
      "content": "<p>Here is some reading material that might be relevant for this competition. Please add/share anything that might be useful or relevant :)</p>\n\n<h3>Resources for Generative Networks</h3>\n\n<ul>\n<li><a href=\"http://cs229.stanford.edu/notes/cs229-notes2.pdf\">“Generative Learning algorithms” - Andrew Ng’s Stanford notes</a></li>\n<li><a href=\"http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf\">On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, by Andrew Ng and Michael I. Jordan</a></li>\n<li><a href=\"https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html\">The Math Behind Generative Adversarial Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/1812.04948\">A Style-Based Generator Architecture for Generative Adversarial Networks, Code – Examples of StyleGAN in action: Faces, Anime, Art – Description of the StyleGAN architecture</a></li>\n<li><a href=\"https://github.com/hamaadshah/gan_deeplearning4j\">Automatic feature engineering using Generative Adversarial Networks with Deeplearning4j &amp; Spark</a></li>\n<li><a href=\"https://arxiv.org/pdf/1906.00446.pdf\">Generating Diverse High-Fidelity Images with VQ-VAE-2</a></li>\n</ul>\n\n<h3>GAN Use Cases</h3>\n\n<ul>\n<li><a href=\"https://arxiv.org/abs/1605.05396\">Text to Image Generation</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.07004\">Image to Image Translation</a></li>\n<li><a href=\"https://arxiv.org/abs/1609.04802\">Increasing Image Resolution</a></li>\n<li><a href=\"https://arxiv.org/abs/1511.06380\">Predicting Next Video Frame</a></li>\n</ul>\n\n<h3>Notable Papers on GANs</h3>\n\n<ul>\n<li><strong>Generative Adversarial Nets</strong> <a href=\"https://arxiv.org/abs/1406.2661\">Paper</a> <a href=\"https://github.com/goodfeli/adversarial\">Code</a>(Ian Goodfellow’s breakthrough paper)</li>\n<li><a href=\"https://github.com/soumith/ganhacks\">GAN Hacks: How to Train a GAN? Tips and tricks to make GANs work</a></li>\n</ul>\n\n<h3>Tutorials</h3>\n\n<ul>\n<li><a href=\"https://sites.google.com/view/iccv-2017-gans/schedule\">ICCV 2017 Tutorial About GANS</a></li>\n</ul>\n\n<p><br></p>\n\n<hr>\n\n<p><strong>Source: <a href=\"https://skymind.ai/wiki/generative-adversarial-network-gan\">A Beginner's Guide to Generative Adversarial Networks (GANs)</a></strong></p>",
      "rawMarkdown": "Here is some reading material that might be relevant for this competition. Please add/share anything that might be useful or relevant :)\n\n### Resources for Generative Networks\n\n- [“Generative Learning algorithms” - Andrew Ng’s Stanford notes](http://cs229.stanford.edu/notes/cs229-notes2.pdf)\n- [On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, by Andrew Ng and Michael I. Jordan](http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf)\n- [The Math Behind Generative Adversarial Networks](https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html)\n- [A Style-Based Generator Architecture for Generative Adversarial Networks, Code – Examples of StyleGAN in action: Faces, Anime, Art – Description of the StyleGAN architecture](https://arxiv.org/abs/1812.04948)\n- [Automatic feature engineering using Generative Adversarial Networks with Deeplearning4j &amp; Spark](https://github.com/hamaadshah/gan_deeplearning4j)\n- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://arxiv.org/pdf/1906.00446.pdf)\n\n### GAN Use Cases\n- [Text to Image Generation](https://arxiv.org/abs/1605.05396)\n- [Image to Image Translation](https://arxiv.org/abs/1611.07004)\n- [Increasing Image Resolution](https://arxiv.org/abs/1609.04802)\n- [Predicting Next Video Frame](https://arxiv.org/abs/1511.06380)\n\n### Notable Papers on GANs\n\n- **Generative Adversarial Nets** [Paper](https://arxiv.org/abs/1406.2661) [Code](https://github.com/goodfeli/adversarial)(Ian Goodfellow’s breakthrough paper)\n- [GAN Hacks: How to Train a GAN? Tips and tricks to make GANs work](https://github.com/soumith/ganhacks)\n\n### Tutorials\n\n- [ICCV 2017 Tutorial About GANS](https://sites.google.com/view/iccv-2017-gans/schedule)\n\n<br>\n\n---\n\n**Source: [A Beginner's Guide to Generative Adversarial Networks (GANs)](https://skymind.ai/wiki/generative-adversarial-network-gan)**",
      "votes": null
    },
    {
      "id": "564086",
      "postDate": "06/29/2019 00:49:17",
      "content": "<p>looks very useful, Thanks!</p>",
      "rawMarkdown": "looks very useful, Thanks!",
      "votes": null
    },
    {
      "id": "564146",
      "postDate": "06/29/2019 03:28:45",
      "content": "<p>very good discussion,!</p>",
      "rawMarkdown": "very good discussion,!",
      "votes": null
    },
    {
      "id": "564177",
      "postDate": "06/29/2019 04:56:24",
      "content": "<p>Thanks.</p>",
      "rawMarkdown": "Thanks.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 564086,
      "author_name": "ttahara",
      "author_url": "",
      "post_date": "06/29/2019 00:49:17",
      "content": "<p>looks very useful, Thanks!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 564146,
      "author_name": "snakayama",
      "author_url": "",
      "post_date": "06/29/2019 03:28:45",
      "content": "<p>very good discussion,!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 564177,
      "author_name": "pulkitmehtawork1985",
      "author_url": "",
      "post_date": "06/29/2019 04:56:24",
      "content": "<p>Thanks.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "564016": "Here is some reading material that might be relevant for this competition. Please add/share anything that might be useful or relevant :)\n\n### Resources for Generative Networks\n\n- [“Generative Learning algorithms” - Andrew Ng’s Stanford notes](http://cs229.stanford.edu/notes/cs229-notes2.pdf)\n- [On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes, by Andrew Ng and Michael I. Jordan](http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf)\n- [The Math Behind Generative Adversarial Networks](https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html)\n- [A Style-Based Generator Architecture for Generative Adversarial Networks, Code – Examples of StyleGAN in action: Faces, Anime, Art – Description of the StyleGAN architecture](https://arxiv.org/abs/1812.04948)\n- [Automatic feature engineering using Generative Adversarial Networks with Deeplearning4j &amp; Spark](https://github.com/hamaadshah/gan_deeplearning4j)\n- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://arxiv.org/pdf/1906.00446.pdf)\n\n### GAN Use Cases\n- [Text to Image Generation](https://arxiv.org/abs/1605.05396)\n- [Image to Image Translation](https://arxiv.org/abs/1611.07004)\n- [Increasing Image Resolution](https://arxiv.org/abs/1609.04802)\n- [Predicting Next Video Frame](https://arxiv.org/abs/1511.06380)\n\n### Notable Papers on GANs\n\n- **Generative Adversarial Nets** [Paper](https://arxiv.org/abs/1406.2661) [Code](https://github.com/goodfeli/adversarial)(Ian Goodfellow’s breakthrough paper)\n- [GAN Hacks: How to Train a GAN? Tips and tricks to make GANs work](https://github.com/soumith/ganhacks)\n\n### Tutorials\n\n- [ICCV 2017 Tutorial About GANS](https://sites.google.com/view/iccv-2017-gans/schedule)\n\n<br>\n\n---\n\n**Source: [A Beginner's Guide to Generative Adversarial Networks (GANs)](https://skymind.ai/wiki/generative-adversarial-network-gan)**",
    "564086": "looks very useful, Thanks!",
    "564146": "very good discussion,!",
    "564177": "Thanks."
  },
  "source": "meta"
}