<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Quality on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/model-quality/</link><description>Recent content in Model Quality on 中文AI术语词典</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 18 Jul 2026 11:44:45 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/zh/tags/model-quality/index.xml" rel="self" type="application/rss+xml"/><item><title>鲁棒性</title><link>https://terms-en.ai-term-hub.com/zh/terms/robust/</link><pubDate>Sat, 18 Jul 2026 10:54:27 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/robust/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>在人工智能中，鲁棒性指模型对抗攻击、数据分布偏移或噪声输入的韧性。一个具有鲁棒性的算法即使在面对干扰时也能继续正确运行。&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>描述AI模型或系统在面临噪声、错误或意外输入时保持性能的能力。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>对抗韧性&lt;/li>
&lt;li>泛化&lt;/li>
&lt;li>噪声容忍度&lt;/li>
&lt;li>稳定性&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>恶劣天气下的自动驾驶&lt;/li>
&lt;li>带有噪声数据的欺诈检测&lt;/li>
&lt;li>记录不全的医疗诊断&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/regularization-%E6%AD%A3%E5%88%99%E5%8C%96/">Regularization (正则化)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/adversarial-attacks-%E5%AF%B9%E6%8A%97%E6%94%BB%E5%87%BB/">Adversarial Attacks (对抗攻击)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/generalization-%E6%B3%9B%E5%8C%96/">Generalization (泛化)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>