https://hackerpaper.kaipfister.de/p/1511.05644/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=11806039urn:hackerpapers:submission:1511.05644:11806039Tue, 31 May 2016 12:05:20 GMT<p>Adversarial Autoencoders</p><p></p><p>3 points; 0 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/1511.05644/">Paper and source history</a>. A submission is not an endorsement.</p>cs.LGhttps://news.ycombinator.com/item?id=10930709urn:hackerpapers:mention:1511.05644:10930709Tue, 19 Jan 2016 13:53:30 GMT<p>If we really want to get into &quot;what is better&quot; - the state of the art has recently moved significantly past this (paper [1] and blog [2]) - not to mention the recent submissions to ICLR in this realm [3][4]. GAN training is very tricky to get working in practice, especially on new problems, though conditional GAN seems to be a bit more stable in learning than the unconditional relatives. This blog deserves credit for thinking differently about a problem and getting something useful out of it. I think the goal of this inversion was to inspect what the cascade is &quot;looking at&quot; in some sense…</p><p><a href="https://hackerpaper.kaipfister.de/p/1511.05644/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG