{
  "id": 185910,
  "title": "Top 10 papers to get stated with GANs",
  "url": "/competitions/gan-getting-started/discussion/185910",
  "author_name": "",
  "post_date": "2020-09-22T15:00:27.694873300Z",
  "votes": 23,
  "comment_count": 7,
  "views": 0,
  "content": "<p>10 interesting papers about GANs</p>\n<ol>\n<li><a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\">DCGANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1606.03498\">Improved Techniques for Training GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1411.1784\">Conditional GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1710.10196\">Progressively Growing GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1809.11096\">BigGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1812.04948\">StyleGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1703.10593\">CycleGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.07004\">Pix2Pix</a></li>\n<li><a href=\"https://arxiv.org/abs/1612.03242\">StackGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a></li>\n</ol>\n<h3>References</h3>\n<ul>\n<li><a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\" target=\"_blank\">https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317</a></li>\n<li><a href=\"https://towardsdatascience.com/what-is-a-gan-d201752ec615\" target=\"_blank\">https://towardsdatascience.com/what-is-a-gan-d201752ec615</a></li>\n</ul>",
  "messages": [
    {
      "id": "1022464",
      "postDate": "09/22/2020 15:00:27",
      "content": "<p>10 interesting papers about GANs</p>\n<ol>\n<li><a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\">DCGANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1606.03498\">Improved Techniques for Training GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1411.1784\">Conditional GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1710.10196\">Progressively Growing GANs</a></li>\n<li><a href=\"https://arxiv.org/abs/1809.11096\">BigGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1812.04948\">StyleGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1703.10593\">CycleGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.07004\">Pix2Pix</a></li>\n<li><a href=\"https://arxiv.org/abs/1612.03242\">StackGAN</a></li>\n<li><a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a></li>\n</ol>\n<h3>References</h3>\n<ul>\n<li><a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\" target=\"_blank\">https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317</a></li>\n<li><a href=\"https://towardsdatascience.com/what-is-a-gan-d201752ec615\" target=\"_blank\">https://towardsdatascience.com/what-is-a-gan-d201752ec615</a></li>\n</ul>",
      "rawMarkdown": "10 interesting papers about GANs\n\n1. <a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\">DCGANs</a>\n2. <a href=\"https://arxiv.org/abs/1606.03498\">Improved Techniques for Training GANs</a>\n3. <a href=\"https://arxiv.org/abs/1411.1784\">Conditional GANs</a>\n4. <a href=\"https://arxiv.org/abs/1710.10196\">Progressively Growing GANs</a>\n5. <a href=\"https://arxiv.org/abs/1809.11096\">BigGAN</a>\n6. <a href=\"https://arxiv.org/abs/1812.04948\">StyleGAN</a>\n7. <a href=\"https://arxiv.org/abs/1703.10593\">CycleGAN</a>\n8. <a href=\"https://arxiv.org/abs/1611.07004\">Pix2Pix</a>\n9. <a href=\"https://arxiv.org/abs/1612.03242\">StackGAN</a>\n10. <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>\n\n### References\n* https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\n* https://towardsdatascience.com/what-is-a-gan-d201752ec615",
      "votes": null
    },
    {
      "id": "1059171",
      "postDate": "10/24/2020 17:51:10",
      "content": "<p>Merci beaucoup. ç'est gentil.👍👍👍</p>",
      "rawMarkdown": "Merci beaucoup. ç'est gentil.👍👍👍",
      "votes": null
    },
    {
      "id": "1232637",
      "postDate": "03/09/2021 22:10:14",
      "content": "<p>Thank You!</p>",
      "rawMarkdown": "Thank You!",
      "votes": null
    },
    {
      "id": "1501784",
      "postDate": "09/03/2021 14:22:50",
      "content": "<p>Thanks a lot, that's what I am looking for!</p>",
      "rawMarkdown": "Thanks a lot, that's what I am looking for!",
      "votes": null
    },
    {
      "id": "1549976",
      "postDate": "10/19/2021 10:32:58",
      "content": "<p>Thanks a lot for the compilation</p>",
      "rawMarkdown": "Thanks a lot for the compilation",
      "votes": null
    },
    {
      "id": "1709609",
      "postDate": "03/02/2022 11:06:09",
      "content": "<p>Thanks a lot for sharing.</p>",
      "rawMarkdown": "Thanks a lot for sharing.",
      "votes": null
    },
    {
      "id": "2732274",
      "postDate": "04/03/2024 04:13:17",
      "content": "<p>Thanks for sharing! Good set of resources.</p>",
      "rawMarkdown": "Thanks for sharing! Good set of resources.",
      "votes": null
    },
    {
      "id": "2786163",
      "postDate": "05/01/2024 07:33:34",
      "content": "<p>thank you for sharing this.</p>",
      "rawMarkdown": "thank you for sharing this.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1059171,
      "author_name": "dinhthilan",
      "author_url": "",
      "post_date": "10/24/2020 17:51:10",
      "content": "<p>Merci beaucoup. ç'est gentil.👍👍👍</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1232637,
      "author_name": "annikasquires",
      "author_url": "",
      "post_date": "03/09/2021 22:10:14",
      "content": "<p>Thank You!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1501784,
      "author_name": "lonnieqin",
      "author_url": "",
      "post_date": "09/03/2021 14:22:50",
      "content": "<p>Thanks a lot, that's what I am looking for!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1549976,
      "author_name": "sandeepvja",
      "author_url": "",
      "post_date": "10/19/2021 10:32:58",
      "content": "<p>Thanks a lot for the compilation</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1709609,
      "author_name": "nanditab35",
      "author_url": "",
      "post_date": "03/02/2022 11:06:09",
      "content": "<p>Thanks a lot for sharing.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2732274,
      "author_name": "ashishsabraham",
      "author_url": "",
      "post_date": "04/03/2024 04:13:17",
      "content": "<p>Thanks for sharing! Good set of resources.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2786163,
      "author_name": "adyatmawany",
      "author_url": "",
      "post_date": "05/01/2024 07:33:34",
      "content": "<p>thank you for sharing this.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1022464": "10 interesting papers about GANs\n\n1. <a href=\"https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\">DCGANs</a>\n2. <a href=\"https://arxiv.org/abs/1606.03498\">Improved Techniques for Training GANs</a>\n3. <a href=\"https://arxiv.org/abs/1411.1784\">Conditional GANs</a>\n4. <a href=\"https://arxiv.org/abs/1710.10196\">Progressively Growing GANs</a>\n5. <a href=\"https://arxiv.org/abs/1809.11096\">BigGAN</a>\n6. <a href=\"https://arxiv.org/abs/1812.04948\">StyleGAN</a>\n7. <a href=\"https://arxiv.org/abs/1703.10593\">CycleGAN</a>\n8. <a href=\"https://arxiv.org/abs/1611.07004\">Pix2Pix</a>\n9. <a href=\"https://arxiv.org/abs/1612.03242\">StackGAN</a>\n10. <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>\n\n### References\n* https://towardsdatascience.com/must-read-papers-on-gans-b665bbae3317\n* https://towardsdatascience.com/what-is-a-gan-d201752ec615",
    "1059171": "Merci beaucoup. ç'est gentil.👍👍👍",
    "1232637": "Thank You!",
    "1501784": "Thanks a lot, that's what I am looking for!",
    "1549976": "Thanks a lot for the compilation",
    "1709609": "Thanks a lot for sharing.",
    "2732274": "Thanks for sharing! Good set of resources.",
    "2786163": "thank you for sharing this."
  },
  "source": "meta"
}