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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.02680/</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/1511.02680/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding — mentioned by ebalit</title><link>https://news.ycombinator.com/item?id=12939837</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.02680:12939837</guid><pubDate>Sat, 12 Nov 2016 19:54:44 GMT</pubDate><description>&lt;p&gt;You can use a model that gives you its classification uncertainty. Bayesian SegNet for example [1]. We may also adapt the legislation for how vehicles should look like we did to make human driving easier (ex: tail and side lights). 1: https://arxiv.org/abs/1511.02680&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.02680/"&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>
