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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PlaNet - Photo Geolocation with Convolutional Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1602.05314/</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/1602.05314/feed.xml" rel="self" type="application/rss+xml" /><item><title>PlaNet - Photo Geolocation with Convolutional Neural Networks — mentioned by jefftk</title><link>https://news.ycombinator.com/item?id=21594160</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.05314:21594160</guid><pubDate>Thu, 21 Nov 2019 14:14:33 GMT</pubDate><description>&lt;p&gt;Those pictures are not sufficiently anonymous; https://arxiv.org/abs/1602.05314 or just neighbors could easily see where she is. And the barcodes are easily visible on some of the boxes.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — mentioned by aub3bhat</title><link>https://news.ycombinator.com/item?id=14454653</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.05314:14454653</guid><pubDate>Wed, 31 May 2017 18:08:41 GMT</pubDate><description>&lt;p&gt;There are so many issues with this post, let me enumerate: 1. Straw man tweet by some non-practitioner which is used to set up the straw-man argument. 2. The whole Digits example is ridiculous, statisticians &amp;quot;love&amp;quot; toy problems to prove theorems &amp;amp; make &amp;quot;arguments&amp;quot; etc. ML is empirical and not just the performance but the entire pipeline from data to application matters. Let me illustrate: If your aim is to predict 1 vs 0 from images of digits. As an ML researcher I would write a program to synthesize images in all different combinations of fonts/font-color/background color/ location…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=13787987</link><guid isPermaLink="false">urn:hackerpapers:submission:1602.05314:13787987</guid><pubDate>Fri, 03 Mar 2017 23:43:10 GMT</pubDate><description>&lt;p&gt;PlaNet – Photo Geolocation with Convolutional Neural Networks&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — mentioned by epistasis</title><link>https://news.ycombinator.com/item?id=13039918</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.05314:13039918</guid><pubDate>Fri, 25 Nov 2016 21:20:08 GMT</pubDate><description>&lt;p&gt;Presumably more accurate than a single human, and you can do it with multiple humans and reaching a consensus. I remember an anecdote in physics class where an experiment required counting a certain number of events in time. A single person would occasionally blink and miss an event. But if you had two people, and you count how many people observed each event, you can solve for super-human accuracy using the estimated error rates of each person. See also this usage in the context of ML: https://arxiv.org/pdf/1602.05314v1.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — mentioned by daveloyall</title><link>https://news.ycombinator.com/item?id=11498922</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.05314:11498922</guid><pubDate>Thu, 14 Apr 2016 17:50:19 GMT</pubDate><description>&lt;p&gt;Google did this already. http://arxiv.org/pdf/1602.05314.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=11294865</link><guid isPermaLink="false">urn:hackerpapers:submission:1602.05314:11294865</guid><pubDate>Wed, 16 Mar 2016 03:47:48 GMT</pubDate><description>&lt;p&gt;PlaNet – Photo Geolocation with Convolutional Neural Networks&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/1602.05314/"&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>PlaNet - Photo Geolocation with Convolutional Neural Networks — mentioned by brudgers</title><link>https://news.ycombinator.com/item?id=11209921</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.05314:11209921</guid><pubDate>Wed, 02 Mar 2016 14:00:14 GMT</pubDate><description>&lt;p&gt;Relevant paper: http://arxiv.org/pdf/1602.05314v1.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.05314/"&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>
