Uncertainty Quantification in Video Distortion Classification Under Dataset Shift

doi:10.1007/978-3-032-04546-1_36

2026-01-01

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Riestiya Zain Fadillah *

Seyed Ali Amirshahi

Marius Pedersen

Azeddine Beghdadi

DOI：10.1007/978-3-032-04546-1_36

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虽然ResNet18、Inception-ResNet-v2和ViT等模型在基准数据集上实现了高精度，但其在现实世界变化下的可靠性仍不确定。本研究聚焦于分析数据集变化下的视频失真分类，特别强调理解模型不确定性和表示行为。我们利用蒙特卡洛 dropout 研究概念漂移和协变量漂移如何影响模型性能。具有强烈且全局一致视觉特征的失真对概念漂移表现出更强的鲁棒性，而空间变化的失真更容易引起混淆。在协变量漂移下，在更细微的失真上训练可提高泛化能力并稳定不确定性，起到隐式正则化的作用。这些发现揭示了当前监督模型的关键局限性，并促使需要更鲁棒、不确定性感知的方法用于现实世界视频失真分类。

Keyword:

Distortion Classification

Uncertainty Quantification

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ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING-ICANN 2025, PT II

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norwegian university of science & technology (ntnu)

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