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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.01854/</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/1610.01854/feed.xml" rel="self" type="application/rss+xml" /><item><title>Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning — mentioned by nl</title><link>https://news.ycombinator.com/item?id=12992457</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.01854:12992457</guid><pubDate>Sat, 19 Nov 2016 07:23:58 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;d note that they claim 89.5% accuracy(!) using the CNN classifier. One paper they reference[1] use a similar technique to attempt the (seemingly much easier) task of classifying people into Chinese, Korean or Japanese. They get 75% accuracy. 89% accuracy means that there is almost no other feature that influences criminality. That should set off all kinds of alarms. If there was some kind of relationship between facial features and criminality (and I don&amp;#x27;t discount that there could be) I&amp;#x27;d expect it to be a very weak one, not one that is accurate 9/10 times. [1]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.01854/"&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>
