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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Differentially Private Generative Adversarial Network · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1802.06739/</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/1802.06739/feed.xml" rel="self" type="application/rss+xml" /><item><title>Differentially Private Generative Adversarial Network — mentioned by snowpid</title><link>https://news.ycombinator.com/item?id=34338256</link><guid isPermaLink="false">urn:hackerpapers:mention:1802.06739:34338256</guid><pubDate>Wed, 11 Jan 2023 13:39:31 GMT</pubDate><description>&lt;p&gt;If the data is sparse, some researches used AI generated content to train another network. E.G. https://arxiv.org/pdf/1802.06739.pdf In some cases like here it also has privacy issues.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1802.06739/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Differentially Private Generative Adversarial Network — mentioned by jaster</title><link>https://news.ycombinator.com/item?id=16464110</link><guid isPermaLink="false">urn:hackerpapers:mention:1802.06739:16464110</guid><pubDate>Mon, 26 Feb 2018 09:16:19 GMT</pubDate><description>&lt;p&gt;Fun coincidence (or maybe not?), I just stumbled upon the following paper on arvix titled &amp;quot;Differentially Private Generative Adversarial Network&amp;quot; https://arxiv.org/pdf/1802.06739v1.pdf Its Related Work section also references some works on &amp;quot;differentially private learning in neural network&amp;quot;. While the paper does not discuss directly OP remark about how differential privacy could help generality in machine learning, it shows that at least some connections exist between the domains.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1802.06739/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
