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English(EN) LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks

新的 LiST 方法增强了神经网络的准确性、鲁棒性和校准性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为 Lipschitz Scaling Training (LiST) 的新方法，旨在同时提高神经网络的准确性、鲁棒性和校准性。LiST 在 Lipschitz 约束和温度缩放（一种校准技术）之间建立了理论和经验联系。通过在训练过程中迭代调整 Lipschitz 常数，LiST 在准确性-鲁棒性权衡曲线上识别出一个最佳操作点，同时确保了校准性。该方法已在 CIFAR-10/100 和 Tiny-ImageNet 等数据集上得到验证，与现有基线相比表现出有竞争力的性能。 AI

影响 这项研究提供了一种训练更可靠神经网络的新方法，有望提高其在安全关键应用中的性能。

排序理由 该集群包含一篇详细介绍神经网络新训练方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的 LiST 方法增强了神经网络的准确性、鲁棒性和校准性

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Arthur Chiron (IRIT, EPE UT), Franck Mamalet (IRIT, DTIPG - SNCF, UT3), Thomas Massena (IRIT, DTIPG - SNCF, UT3), Thomas Deltort (IRIT), Mathieu Serrurier (IRIT, UT2J) ·

    LiST：用于鲁棒和校准神经网络的Lipschitz缩放训练

    arXiv:2607.07745v1 Announce Type: cross Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge. Lipschitz-constrai…

  2. arXiv stat.ML TIER_1 English(EN) · Mathieu Serrurier ·

    LiST：用于鲁棒和校准神经网络的 Lipschitz 缩放训练

    While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge. Lipschitz-constrained models guarantee robustness by design, yet the…

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