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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.02719/</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/1712.02719/feed.xml" rel="self" type="application/rss+xml" /><item><title>Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing — mentioned by ben_w</title><link>https://news.ycombinator.com/item?id=18656382</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.02719:18656382</guid><pubDate>Tue, 11 Dec 2018 16:25:07 GMT</pubDate><description>&lt;p&gt;&amp;gt; We&amp;#x27;re not deep learning systems. So reasoning by analogy there isn&amp;#x27;t correct. That’s why I’m asking which definition you’re using. It’s broad enough that some models are biologically plausible even though there is criticism that many others are not: https://arxiv.org/abs/1502.04156 &amp;gt; Deep learning systems, are examples of supervised learning. Once trained, they cannot adapt to new input. You have to retrain to accommodate new features that you want to capture. Supervised, unsupervised, and reinforcement; and there is work on both incremental learning (https://arxiv.org/abs/1712.02719) and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.02719/"&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>
