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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.04861/</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/1704.04861/feed.xml" rel="self" type="application/rss+xml" /><item><title>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — mentioned by fxtentacle</title><link>https://news.ycombinator.com/item?id=22409252</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.04861:22409252</guid><pubDate>Mon, 24 Feb 2020 22:44:39 GMT</pubDate><description>&lt;p&gt;The algorithms that run on cell phones tend to be specially optimized and quality-reduced neural networks. For example, https://arxiv.org/abs/1704.04861&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — mentioned by bwasti</title><link>https://news.ycombinator.com/item?id=14834515</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.04861:14834515</guid><pubDate>Sun, 23 Jul 2017 21:32:08 GMT</pubDate><description>&lt;p&gt;I doubt pruning alone is going to serve up the same kind of wins that energy aware architectures will. The main problem is that convolutions are heavy computational operations. There has been ongoing research in modifications to the convolution that basically decrease the amount of computation being done in channel space (grouped and depthwise separable convolutions). The tradeoff is accuracy, but new techniques (like shuffling the channels) are helping recover that. MobileNets and ShuffleNet are some examples. They see 10x reductions in MACs, far more than the &amp;quot;73 percent reduction in power…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — HN submission</title><link>https://news.ycombinator.com/item?id=14572032</link><guid isPermaLink="false">urn:hackerpapers:submission:1704.04861:14572032</guid><pubDate>Fri, 16 Jun 2017 19:39:50 GMT</pubDate><description>&lt;p&gt;MobileNets: Efficient Convolutional Neural Networks for Mobile Vision&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — mentioned by ip_addr</title><link>https://news.ycombinator.com/item?id=14350874</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.04861:14350874</guid><pubDate>Tue, 16 May 2017 16:31:11 GMT</pubDate><description>&lt;p&gt;I believe they are referring to this paper titled &amp;quot;MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications&amp;quot; https://arxiv.org/pdf/1704.04861.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — HN submission</title><link>https://news.ycombinator.com/item?id=14146597</link><guid isPermaLink="false">urn:hackerpapers:submission:1704.04861:14146597</guid><pubDate>Wed, 19 Apr 2017 09:50:40 GMT</pubDate><description>&lt;p&gt;MobileNets: Efficient Convolutional Neural Networks for Mobile Vision&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — HN submission</title><link>https://news.ycombinator.com/item?id=14137950</link><guid isPermaLink="false">urn:hackerpapers:submission:1704.04861:14137950</guid><pubDate>Tue, 18 Apr 2017 11:32:55 GMT</pubDate><description>&lt;p&gt;Google&amp;#x27;s MobileNet architecture for small and scalable image classifiers is out&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1704.04861/"&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>
