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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1406.6909/</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/1406.6909/feed.xml" rel="self" type="application/rss+xml" /><item><title>Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks — mentioned by tmp65535</title><link>https://news.ycombinator.com/item?id=29737947</link><guid isPermaLink="false">urn:hackerpapers:mention:1406.6909:29737947</guid><pubDate>Thu, 30 Dec 2021 16:16:33 GMT</pubDate><description>&lt;p&gt;Here is the first (?) application of deep learning to improve the pornography experience, an Android app called Melondream published in early 2017: http://driftwheeler.com Surprisingly, almost 5 years after being published, this app still serves thousands of user sessions per day. That rate continues to slowly grow. Except for its user interface, the whole app (client and server) was written in Go (https://pkg.go.dev/golang.org/x/mobile/cmd/gomobile). It uses densenet feature vectors (https://arxiv.org/abs/1608.06993) to allow &amp;quot;touch-to-search&amp;quot; (ha!) with a fractional norm distance metric…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1406.6909/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks — mentioned by driftwheeler</title><link>https://news.ycombinator.com/item?id=15060436</link><guid isPermaLink="false">urn:hackerpapers:mention:1406.6909:15060436</guid><pubDate>Sun, 20 Aug 2017 21:55:09 GMT</pubDate><description>&lt;p&gt;Citation: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (https://arxiv.org/abs/1406.6909) 50,000 MetArt-style nudes. No banners, no ads, no hassles, no distractions. Smart image zoom to fit the woman to the screen. See random pics (WANDER). When you like one, see the photoshoot on repeat (TRANCE). To search for something-- e.g., grass, beach, face-- long press the image (DREAM). This finds nearest neighbors in deep feature space, but only works reliably for simple concepts because it&amp;#x27;s fully unsupervised. The features you selected by long pressing…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1406.6909/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=14884909</link><guid isPermaLink="false">urn:hackerpapers:submission:1406.6909:14884909</guid><pubDate>Sun, 30 Jul 2017 07:46:46 GMT</pubDate><description>&lt;p&gt;Show HN: Exemplar Networks for a Good Time (NSFW)&lt;/p&gt;&lt;p&gt;http://driftwheeler.com &amp;lt;--- Download APK here. Citation: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (https://arxiv.org/abs/1406.6909) 50,000 MetArt-style nudes. No banners, no ads, no hassles, no distractions. Smart image zoom to fit the woman to the screen. See random pics (WANDER). When you like one, see the photoshoot on repeat (TRANCE). To search for something-- e.g., grass, beach, face, pussy-- long press the image (DREAM). This finds nearest neighbors in deep feature space, but only works reliably for simple concepts because it&amp;#x27;s fully…&lt;/p&gt;&lt;p&gt;3 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1406.6909/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
