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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Domain Adaptive Neural Networks for Object Recognition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1409.6041/</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/1409.6041/feed.xml" rel="self" type="application/rss+xml" /><item><title>Domain Adaptive Neural Networks for Object Recognition — mentioned by tysam_and</title><link>https://news.ycombinator.com/item?id=35996725</link><guid isPermaLink="false">urn:hackerpapers:mention:1409.6041:35996725</guid><pubDate>Fri, 19 May 2023 01:35:00 GMT</pubDate><description>&lt;p&gt;If this were truly a blackhat effort, it could improve the quality of empirical papers at conferences specifically by practically forcing a reproducible-by-the-conference-XYZ org of the results on some benchmark using a Docker image, etc etc. On the other hand, for investigative or more purely academic research, this could be another layer of nightmarish crap that people would have to sift through. Also it looks like at least one example is basically cloned from the source set, just with...basically greatly reduced recall of the content (beyond some high points). This feels nightmarish, to be…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1409.6041/"&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>
