<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Robustness on 111qqz的小窝</title><link>https://111qqz.com/tags/robustness/</link><description>Recent content in Robustness on 111qqz的小窝</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><managingEditor>hust.111qqz@gmail.com (111qqz)</managingEditor><webMaster>hust.111qqz@gmail.com (111qqz)</webMaster><copyright>© 2011-2026 111qqz</copyright><lastBuildDate>Fri, 16 Oct 2026 22:25:00 +0800</lastBuildDate><atom:link href="https://111qqz.com/tags/robustness/index.xml" rel="self" type="application/rss+xml"/><item><title>从 FGSM 理解对抗攻击：泛化好的模型为什么还会被微小扰动欺骗？</title><link>https://111qqz.com/2026/10/adversarial-attacks-fgsm/</link><pubDate>Fri, 16 Oct 2026 22:25:00 +0800</pubDate><author>hust.111qqz@gmail.com (111qqz)</author><guid>https://111qqz.com/2026/10/adversarial-attacks-fgsm/</guid><description>&lt;h2 class="relative group"&gt;起因
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&lt;p&gt;&lt;a href="https://111qqz.com/2026/10/rethinking-generalization-random-labels/" &gt;上一篇&lt;/a&gt;写完随机标签实验之后，我对泛化的理解更新了一轮：模型有能力记住随机标签，不代表它面对真实数据时只会死记硬背。capacity、learned function 和 generalization 是三件不同的事。&lt;/p&gt;</description></item></channel></rss>