{ "id": 179906, "title": "Reading Material for GANs...", "url": "/competitions/gan-getting-started/discussion/179906", "author_name": "Raj Mehrotra", "post_date": "2020-09-03T08:33:55.088000", "votes": 0, "comment_count": 0, "views": 0, "content": "

List of some online reading material on GANs that maybe relevant for this competition. This list is by no means exhaustive and more resources can be added to the list :)

\n

Blogs:

\n
    \n

  1. A Nice Intro to GANs

    2. \n

  2. A Medium Blog on Different applications of GANs

    3. \n

  3. Introductory Blog Post on GANs

    4. \n

  4. Introduction with Code in Pytorch to generate digits

    5. \n

  5. A Nice List of research papers on GAN

    6. \n

  6. Another List of Papers on GAN

    7. \n

  7. Open AI's post on Generative Models

    8. \n

  8. The Maths Behind GANs

    9. \n

  9. Tensorflow Tutorial on CycleGAN

    10. \n

  10. GAN Archives on ML Mastery by Jason Brownlee

    11. \n
\n

Important Research Papers:

\n
    \n

  1. Generative Adversarial Nets

    2. \n

  2. DCGAN Paper

    3. \n

  3. Cycle GAN Paper

    4. \n
", "messages": [ { "id": 996350, "postDate": "2020-09-03T08:33:55.090Z", "content": "

List of some online reading material on GANs that maybe relevant for this competition. This list is by no means exhaustive and more resources can be added to the list :)

\n

Blogs:

\n
    \n

  1. A Nice Intro to GANs

    2. \n

  2. A Medium Blog on Different applications of GANs

    3. \n

  3. Introductory Blog Post on GANs

    4. \n

  4. Introduction with Code in Pytorch to generate digits

    5. \n

  5. A Nice List of research papers on GAN

    6. \n

  6. Another List of Papers on GAN

    7. \n

  7. Open AI's post on Generative Models

    8. \n

  8. The Maths Behind GANs

    9. \n

  9. Tensorflow Tutorial on CycleGAN

    10. \n

  10. GAN Archives on ML Mastery by Jason Brownlee

    11. \n
\n

Important Research Papers:

\n
    \n

  1. Generative Adversarial Nets

    2. \n

  2. DCGAN Paper

    3. \n

  3. Cycle GAN Paper

    4. \n
", "rawMarkdown": "List of some online reading material on GANs that maybe relevant for this competition. This list is by no means exhaustive and more resources can be added to the list :)\n\n# Blogs:\n\n1. [A Nice Intro to GANs](https://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them)\n\n2. [A Medium Blog on Different applications of GANs](https://medium.com/@jonathan_hui/gan-some-cool-applications-of-gans-4c9ecca35900)\n\n3. [Introductory Blog Post on GANs](https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09)\n\n4. [Introduction with Code in Pytorch to generate digits](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f)\n\n5. [A Nice List of research papers on GAN](https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317)\n\n6. [Another List of Papers on GAN](https://deephunt.in/the-gan-zoo-79597dc8c347)\n\n7. [Open AI's post on Generative Models](https://openai.com/blog/generative-models/)\n\n8. [The Maths Behind GANs](https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html)\n\n9. [Tensorflow Tutorial on CycleGAN](https://www.tensorflow.org/tutorials/generative/cyclegan)\n\n10. [GAN Archives on ML Mastery by Jason Brownlee](https://machinelearningmastery.com/category/generative-adversarial-networks/)\n\n\n\n# Important Research Papers:\n\n1. [**Generative Adversarial Nets**](http://papers.nips.cc/paper/5423-generative-adversarial-nets)\n\n2. [DCGAN Paper](https://arxiv.org/abs/1511.06434)\n\n3. [Cycle GAN Paper](https://arxiv.org/abs/1703.10593v6)\n\n\n" } ], "comments": [], "raw_markdown_by_id": { "996350": "List of some online reading material on GANs that maybe relevant for this competition. This list is by no means exhaustive and more resources can be added to the list :)\n\n# Blogs:\n\n1. [A Nice Intro to GANs](https://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them)\n\n2. [A Medium Blog on Different applications of GANs](https://medium.com/@jonathan_hui/gan-some-cool-applications-of-gans-4c9ecca35900)\n\n3. [Introductory Blog Post on GANs](https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09)\n\n4. [Introduction with Code in Pytorch to generate digits](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f)\n\n5. [A Nice List of research papers on GAN](https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317)\n\n6. [Another List of Papers on GAN](https://deephunt.in/the-gan-zoo-79597dc8c347)\n\n7. [Open AI's post on Generative Models](https://openai.com/blog/generative-models/)\n\n8. [The Maths Behind GANs](https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html)\n\n9. [Tensorflow Tutorial on CycleGAN](https://www.tensorflow.org/tutorials/generative/cyclegan)\n\n10. [GAN Archives on ML Mastery by Jason Brownlee](https://machinelearningmastery.com/category/generative-adversarial-networks/)\n\n\n\n# Important Research Papers:\n\n1. [**Generative Adversarial Nets**](http://papers.nips.cc/paper/5423-generative-adversarial-nets)\n\n2. [DCGAN Paper](https://arxiv.org/abs/1511.06434)\n\n3. [Cycle GAN Paper](https://arxiv.org/abs/1703.10593v6)\n\n\n" } }