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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1503.03163/</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/1503.03163/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder — mentioned by AndrewKemendo</title><link>https://news.ycombinator.com/item?id=12496768</link><guid isPermaLink="false">urn:hackerpapers:mention:1503.03163:12496768</guid><pubDate>Wed, 14 Sep 2016 13:45:23 GMT</pubDate><description>&lt;p&gt;Best thing is to probably search around for &amp;quot;Synthetic Training&amp;quot; Here are two examples of some of the research done in this area: http://www.cv-foundation.org/openaccess/content_iccv_2015/pa... https://arxiv.org/pdf/1503.03163.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1503.03163/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
