<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1506.05751/</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/1506.05751/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks — mentioned by throwaway287391</title><link>https://news.ycombinator.com/item?id=13584363</link><guid isPermaLink="false">urn:hackerpapers:mention:1506.05751:13584363</guid><pubDate>Mon, 06 Feb 2017 21:38:43 GMT</pubDate><description>&lt;p&gt;I hate to be a debbie downer but the results don&amp;#x27;t look particularly great to me. For comparison, see &amp;quot;AffGAN&amp;quot; [1] and &amp;quot;LAPGAN&amp;quot; [2] for better (IMO) super-resolution results using GAN-like techniques. Granted, all three papers are applied in somewhat different settings (different input/output resolutions, different datasets), so direct comparison is difficult. [1] https://openreview.net/pdf?id=S1RP6GLle [2] https://arxiv.org/abs/1506.05751&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1506.05751/"&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>
