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English(EN) Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection

新的AGAT方法将对抗性训练时间缩短了70-80%

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为增强引导的对抗性训练（AGAT）的新方法，以提高对抗性训练的效率。AGAT通过识别邻域不稳定的样本，并使用一种称为基于增强的局部稳定性（ALS）的分数，仅将对抗性监督选择性地应用于20-30%的训练样本。这种方法在各种数据集和架构上实现了与标准方法相当或更好的鲁棒准确性，同时显著减少了训练时间和计算成本。 AI

影响 该方法可以显著减少训练鲁棒AI模型所需的计算资源，使其更易于访问和更高效。

排序理由 该集群包含一篇详细介绍对抗性训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的AGAT方法将对抗性训练时间缩短了70-80%

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该集群包含一篇详细介绍对抗性训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Agyei Asante, Mark Tetteh, Deborah Asamoah, Bernard Asante, Regina Esi Turkson ·

    通过基于局部稳定性的样本选择进行增强引导的对抗性训练

    arXiv:2610.33331v1 Announce Type: new Abstract: Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are generated for every training sample. Sele…

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