<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Metric Learning on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/metric-learning/</link><description>Recent content in Metric Learning on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 19 May 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/metric-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Transfer Learning (4): Few-Shot Learning</title><link>https://www.chenk.top/en/transfer-learning/04-few-shot-learning/</link><pubDate>Mon, 19 May 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/transfer-learning/04-few-shot-learning/</guid><description>&lt;p>Show a child one photograph of a pangolin and they will spot pangolins for life. Show a deep learning model one photograph and it will give you a uniformly random guess. Few-shot learning is the field that closes that gap: building classifiers that work with only one to ten labeled examples per class.&lt;/p>
&lt;p>The trick is not to memorize individual classes harder. It is to learn &lt;em>how to learn&lt;/em> from very few examples, then carry that ability over to brand-new classes at test time. This article covers the two families that dominate the field today: &lt;strong>metric learning&lt;/strong>, which learns a good distance function, and &lt;strong>meta-learning&lt;/strong>, which learns a good initialization.&lt;/p></description></item></channel></rss>