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	<title>class-wise robustness disparities &#8211; BIOENGINEER.ORG</title>
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	<title>class-wise robustness disparities &#8211; BIOENGINEER.ORG</title>
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		<title>Enhancing Fair Adversarial Training through Identification and Augmentation of Challenging Examples</title>
		<link>https://bioengineer.org/enhancing-fair-adversarial-training-through-identification-and-augmentation-of-challenging-examples/</link>
		
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		<pubDate>Fri, 28 Mar 2025 16:19:16 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[challenging examples augmentation]]></category>
		<category><![CDATA[class-wise robustness disparities]]></category>
		<category><![CDATA[ethical AI implications]]></category>
		<category><![CDATA[fair adversarial training]]></category>
		<category><![CDATA[FairAT algorithm]]></category>
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					<description><![CDATA[Adversarial training has emerged as the primary line of defense against adversarial attacks in machine learning models, playing a crucial role in enhancing their robustness. However, recent studies reveal a troubling imbalance in the effectiveness of adversarial training across different classes in classification tasks. This class-wise discrepancy in robustness presents significant challenges that could undermine [&#8230;]]]></description>
		
		
		
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