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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2303.03307/</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/2303.03307/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations — mentioned by johnsutor</title><link>https://news.ycombinator.com/item?id=39469569</link><guid isPermaLink="false">urn:hackerpapers:mention:2303.03307:39469569</guid><pubDate>Thu, 22 Feb 2024 16:38:30 GMT</pubDate><description>&lt;p&gt;This is the premise behind some more recent self-supervised learning papers (see https://arxiv.org/abs/2303.03307). Turns out, it&amp;#x27;s a solid analog for classification tasks.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2303.03307/"&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>
