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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Revisiting ResNets: Improved Training and Scaling Strategies · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2103.07579/</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/2103.07579/feed.xml" rel="self" type="application/rss+xml" /><item><title>Revisiting ResNets: Improved Training and Scaling Strategies — mentioned by polyrand</title><link>https://news.ycombinator.com/item?id=26491700</link><guid isPermaLink="false">urn:hackerpapers:mention:2103.07579:26491700</guid><pubDate>Wed, 17 Mar 2021 14:27:57 GMT</pubDate><description>&lt;p&gt;The main challenge with medical data is still the access and ownership. ViT transformers looked good, but only when training on huge amounts of data. Another problem is the distribution of the data, having good results with an internal dataset of a hospital does no guarantee getting the same results with another hospital&amp;#x27;s dataset. I didn&amp;#x27;t know about the HATNet mentioned in the article, but it looks an interesting paper to read. There&amp;#x27;s a recent article[0] showing that a good training strategy with a ResNet can be better than novel architectures. From my experience, a ResNet-34 / ResNet-50…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2103.07579/"&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>
