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English(EN) Improving Context-Shift Robustness of Convolutional Networks via Context-Regularized Cross-Entropy

新的训练方法提高了CNN对抗上下文迁移的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的训练框架，以增强卷积神经网络在上下文迁移现象中的鲁棒性，该现象是指在不同的测试时段环境中模型性能会下降。所提出的方法使用空间特征归因来引导模型关注与对象相关的特征，而不是非因果的上下文线索。这包括一种称为ContrastiveCAMs的新型归因变体和一种改进的损失函数，即上下文正则化交叉熵（CR-CE），它抑制了上下文区域的影响。该方法在Waterbirds、Spawrious和ImageNet变体等多个数据集上持续提高了上下文迁移鲁棒性。 AI

影响 提高了图像分类模型在真实、不可预测环境中的可靠性。

排序理由 该集群包含一篇详细介绍改进机器学习模型新方法的学术论文。[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 →

新的训练方法提高了CNN对抗上下文迁移的鲁棒性

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该集群包含一篇详细介绍改进机器学习模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinen Setpal, Chaoyue Liu ·

    通过上下文正则化交叉熵提高卷积网络的上下文转移鲁棒性

    arXiv:2611.08157v1 Announce Type: cross Abstract: Image classification models often encounter objects in diverse and unpredictable contexts at test time, which may differ substantially from those seen during training. This phenomenon, termed context shift, exposes a key fragility…

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