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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GIFnets: Differentiable GIF Encoding Framework · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2006.13434/</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/2006.13434/feed.xml" rel="self" type="application/rss+xml" /><item><title>GIFnets: Differentiable GIF Encoding Framework — mentioned by dbaupp</title><link>https://news.ycombinator.com/item?id=24491342</link><guid isPermaLink="false">urn:hackerpapers:mention:2006.13434:24491342</guid><pubDate>Wed, 16 Sep 2020 10:47:26 GMT</pubDate><description>&lt;p&gt;Very interesting! This seems somewhat similar to the recently published GIFnets[1]. However, I believe GIFnets is training a reusable network to a predict palettes, and pixel assignments, while this post is focusing on optimising the &amp;quot;weights&amp;quot; (i.e. pixel values) for a single image. I wonder if the loss functions from GIFnets could be applied to this single-image approach to potentially solve the banding problem via something a little more &amp;quot;perceptual&amp;quot; than the variance term mentioned. [1]: &amp;quot;GIFnets: Differentiable GIF Encoding Framework&amp;quot; https://arxiv.org/abs/2006.13434&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2006.13434/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>eess.IV</category></item></channel></rss>
