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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to Discover Cross-Domain Relations with Generative Adversarial Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1703.05192/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1703.05192/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to Discover Cross-Domain Relations with Generative Adversarial Networks — mentioned by risto</title><link>https://news.ycombinator.com/item?id=14460824</link><guid isPermaLink="false">urn:hackerpapers:mention:1703.05192:14460824</guid><pubDate>Thu, 01 Jun 2017 15:04:22 GMT</pubDate><description>&lt;p&gt;SK T-Brain | Research Scientist / Engineer | Seoul, Korea | Depends on experience | Full-time, Onsite, VISA | https://www.facebook.com/notes/sk-t-brain/sk-t-brain-careers... Positions: • AI Research Scientist • AI Research Engineer • AI Software Engineer Are you a researcher who can lead AI research projects? Are you an engineer interested in applying state-of-the-art AI technology? We are an AI research and development team within SK Telecom, the No.1 telecommunications company in South Korea. We are looking for research scientists and engineers who want to lead and participate in research…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1703.05192/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>Learning to Discover Cross-Domain Relations with Generative Adversarial Networks — HN submission</title><link>https://news.ycombinator.com/item?id=14435473</link><guid isPermaLink="false">urn:hackerpapers:submission:1703.05192:14435473</guid><pubDate>Sun, 28 May 2017 18:23:14 GMT</pubDate><description>&lt;p&gt;Learning to Discover Cross-Domain Relations with GANs&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1703.05192/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>Learning to Discover Cross-Domain Relations with Generative Adversarial Networks — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=13968920</link><guid isPermaLink="false">urn:hackerpapers:mention:1703.05192:13968920</guid><pubDate>Mon, 27 Mar 2017 16:12:08 GMT</pubDate><description>&lt;p&gt;I can&amp;#x27;t help but think that DiscoGAN (https://arxiv.org/abs/1703.05192 &amp;quot;Learning to Discover Cross-Domain Relations with Generative Adversarial Networks&amp;quot;, Kim et al 2017) would be perfect for this. Simply feed it a ton of photos of regular clothed people and other naked people (don&amp;#x27;t have to be the same people), and it&amp;#x27;ll learn a mapping on its own. Scale that up, refine it for a few years...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1703.05192/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>Learning to Discover Cross-Domain Relations with Generative Adversarial Networks — HN submission</title><link>https://news.ycombinator.com/item?id=13903742</link><guid isPermaLink="false">urn:hackerpapers:submission:1703.05192:13903742</guid><pubDate>Sat, 18 Mar 2017 22:02:04 GMT</pubDate><description>&lt;p&gt;Learning to Discover Cross-Domain Relations with GANs&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1703.05192/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
