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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1703.09912/</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/1703.09912/feed.xml" rel="self" type="application/rss+xml" /><item><title>One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models — mentioned by radarsat1</title><link>https://news.ycombinator.com/item?id=16938629</link><guid isPermaLink="false">urn:hackerpapers:mention:1703.09912:16938629</guid><pubDate>Fri, 27 Apr 2018 05:25:24 GMT</pubDate><description>&lt;p&gt;I found this one pretty interesting in that regard. The basic gist is that learning the function that projects onto the boundary of the dataset is useful for a variety of (linear inverse) problems. One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models https://arxiv.org/abs/1703.09912&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1703.09912/"&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>
