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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Feature Interpolation for Image Content Changes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.05507/</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/1611.05507/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Feature Interpolation for Image Content Changes — mentioned by webmaven</title><link>https://news.ycombinator.com/item?id=13121542</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.05507:13121542</guid><pubDate>Wed, 07 Dec 2016 10:02:34 GMT</pubDate><description>&lt;p&gt;Probably. I am not aware of any work that checks how accurate such age progression (or regression) transforms are, but there are many papers that use the realism (as evaluated by humans) of the results as a criteria. As one example, here is a recent paper (Deep Feature Interpolation for Image Content Changes) that uses age progression as one of the evaluation tasks: https://arxiv.org/abs/1611.05507 Regardless of the model you choose, if accuracy is your goal your training data may need 2+ images of each subject at different ages, labelled as to age (and possibly year). You might not…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.05507/"&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>
