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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>High-Performance Large-Scale Image Recognition Without Normalization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2102.06171/</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/2102.06171/feed.xml" rel="self" type="application/rss+xml" /><item><title>High-Performance Large-Scale Image Recognition Without Normalization — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=28202837</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.06171:28202837</guid><pubDate>Mon, 16 Aug 2021 20:55:15 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s a couple other papers in the same vein, a genre of &amp;quot;you never needed residual layers or complicated normalization tricks if you just get the initialization &amp;amp; layers right&amp;quot;, like Balduzzi https://arxiv.org/abs/1702.08591 or NFNets https://arxiv.org/abs/2102.06171#deepmind . I find them pretty interesting because they are a datapoint that neural nets were, all along, much more powerful and simpler than we realized, and we just don&amp;#x27;t know how to NN right, and we mistook our incompetence at setting up easily-optimized instances for fundamental limits of the models. Most of that work…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.06171/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>High-Performance Large-Scale Image Recognition Without Normalization — mentioned by igorkraw</title><link>https://news.ycombinator.com/item?id=26658122</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.06171:26658122</guid><pubDate>Thu, 01 Apr 2021 08:57:08 GMT</pubDate><description>&lt;p&gt;Posts like this really piss me off, because you can make anything sound small. So don&amp;#x27;t take the next few paragraphs as me attacking you, I&amp;#x27;m just venting a general sentiment I&amp;#x27;ve had for a while. (Looking back after typing it out, I might actually flesh it out and post it on my blog, I hopeit&amp;#x27;s somewhat thought stimulating for others as well). It&amp;#x27;s like someone saying &amp;quot;well, electrical engineering is really like playing with building blocks. Take zener diodes for example, the problem was that you can have a lot of power in a circuit, but if there&amp;#x27;s a power spike it might break. So they…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.06171/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>High-Performance Large-Scale Image Recognition Without Normalization — HN submission</title><link>https://news.ycombinator.com/item?id=26133798</link><guid isPermaLink="false">urn:hackerpapers:submission:2102.06171:26133798</guid><pubDate>Sun, 14 Feb 2021 16:44:44 GMT</pubDate><description>&lt;p&gt;DeepMind achieves SOTA image recognition with 8.7x faster training&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;291 points; 83 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2102.06171/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>High-Performance Large-Scale Image Recognition Without Normalization — HN submission</title><link>https://news.ycombinator.com/item?id=26119125</link><guid isPermaLink="false">urn:hackerpapers:submission:2102.06171:26119125</guid><pubDate>Fri, 12 Feb 2021 22:10:19 GMT</pubDate><description>&lt;p&gt;High-Performance Large-Scale Image Recognition Without Normalization&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;11 points; 3 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2102.06171/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
