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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>High Fidelity Image Counterfactuals with Probabilistic Causal Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.15764/</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/2306.15764/feed.xml" rel="self" type="application/rss+xml" /><item><title>High Fidelity Image Counterfactuals with Probabilistic Causal Models — mentioned by slothofgalaxy</title><link>https://news.ycombinator.com/item?id=36883344</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.15764:36883344</guid><pubDate>Wed, 26 Jul 2023 19:16:05 GMT</pubDate><description>&lt;p&gt;Ever wondered how we can make our deep generative models respect causal structure? This is key to creating authentic &amp;quot;what if&amp;quot; scenarios in our images! In our latest research, we deal with high-fidelity image counterfactuals, the generation of images based on &amp;quot;what if&amp;quot; scenarios that align with a specified causal graph. Why is this important? Causality gives us the tools to carry out principled counterfactual inference, which - among other things - is useful for maintaining subject identity in image counterfactuals. Principled counterfactuals of structured variables like images have great…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.15764/"&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>
