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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1904.10286/</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/1904.10286/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning — mentioned by Smith42</title><link>https://news.ycombinator.com/item?id=26075897</link><guid isPermaLink="false">urn:hackerpapers:mention:1904.10286:26075897</guid><pubDate>Tue, 09 Feb 2021 10:37:16 GMT</pubDate><description>&lt;p&gt;You can also generate similar scale astronomical images with GANs! I worked on a model that could do this last year: Paper: https://arxiv.org/abs/1904.10286 7.6 gigapixel image: https://star.herts.ac.uk/~jgeach/gdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1904.10286/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>astro-ph.IM</category></item></channel></rss>
