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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learned Image Downscaling for Upscaling using Content Adaptive Resampler · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1907.12904/</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/1907.12904/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learned Image Downscaling for Upscaling using Content Adaptive Resampler — mentioned by skylanh</title><link>https://news.ycombinator.com/item?id=23920316</link><guid isPermaLink="false">urn:hackerpapers:mention:1907.12904:23920316</guid><pubDate>Wed, 22 Jul 2020 18:52:47 GMT</pubDate><description>&lt;p&gt;This was my first impression as well, except with less knowledge on the subject. Someone, somewhere said &amp;quot;huh...&amp;quot;, and placed another filter into the pipeline to handle these types of images. While looking for something to sound smart wrt the tank-training myth, I found this interesting page: https://www.gwern.net/Tanks &amp;quot;The Neural Net Tank Urban Legend&amp;quot; And interestingly, looking at the link for &amp;quot;superresolution needing learned downscalers&amp;quot; found this: https://arxiv.org/abs/1907.12904 &amp;quot;Learned Image Downscaling for Upscaling using Content Adaptive Resampler&amp;quot;, code available at…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1907.12904/"&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>
