<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adversarial Learning on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/adversarial-learning/</link><description>Recent content in Adversarial Learning on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 13 May 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/adversarial-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Transfer Learning (3): Domain Adaptation</title><link>https://www.chenk.top/en/transfer-learning/03-domain-adaptation/</link><pubDate>Tue, 13 May 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/transfer-learning/03-domain-adaptation/</guid><description>&lt;p>Your autonomous-driving stack works perfectly on sunny California freeways. Then it rains in Seattle. Top-1 accuracy drops from 95% to 70%. The model did not get worse — the &lt;em>data distribution shifted&lt;/em>, and your training set never told it what wet asphalt looks like at dusk.&lt;/p>
&lt;p>This is the everyday problem of &lt;strong>domain adaptation&lt;/strong>: you have abundant labelled data in one distribution (the &lt;em>source&lt;/em>) and unlabelled data in another (the &lt;em>target&lt;/em>), and you need the model to perform on the target. This article shows you how, from first-principles theory to a working DANN implementation.&lt;/p></description></item></channel></rss>