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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mining Mid-level Visual Patterns with Deep CNN Activations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1506.06343/</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/1506.06343/feed.xml" rel="self" type="application/rss+xml" /><item><title>Mining Mid-level Visual Patterns with Deep CNN Activations — mentioned by cdoersch</title><link>https://news.ycombinator.com/item?id=17773639</link><guid isPermaLink="false">urn:hackerpapers:mention:1506.06343:17773639</guid><pubDate>Thu, 16 Aug 2018 11:16:17 GMT</pubDate><description>&lt;p&gt;Wow--didn&amp;#x27;t expect my old paper to re-surface in 2018. Besides being a neat visualization, one remarkable thing (to me) is that this is one of the few computer vision tasks where the state-of-the-art is still the old hand-designed descriptors--specifically HOG. The attempts to re-do this work with deep learning haven&amp;#x27;t worked so well. The closest I know is https://arxiv.org/abs/1506.06343 , which I&amp;#x27;ve never even seen applied to cities. The problem is actually that deep nets have too much invariance: they can classify whether a facade is in Paris or not, but there&amp;#x27;s no easy way to separate out…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1506.06343/"&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>
