Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
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DOI：
10.48550/arxiv.2306.03288
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发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Shahana Ibrahim;Tri Nguyen;Xiao Fu
中科院分区：
文献类型：
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作者：
Shahana Ibrahim;Tri Nguyen;Xiao Fu
使用来自多个注释者的嘈杂众包标签，基于深度学习的端到端（E2E）系统旨在同时学习标签校正机制和神经分类器。为此，许多E2E系统将神经分类器与多个注释者特定的``标签混乱''层串联并以参数耦合方式共同训练两个部分。配制的耦合跨透镜最小化（CCEM）型标准是直观的，实践效果很好。但是，对CCEM标准的理论理解是有限的。这项工作的贡献是双重的：首先，提出了CCEM标准的性能保证。我们的分析首次揭示了CCEM确实可以正确地识别注释者的混乱特征以及所需的``地面真相''神经分类器在现实条件下，例如，只有不完整的注释者标记和有限样本就可以使用。其次，根据从我们的分析中学到的见解，提出了两个CCEM的正则变体。正式化术语可证明在各种更具挑战性的情况下增强了目标模型参数的可识别性。提出了一系列合成和真实的数据实验，以展示我们方法的有效性。
Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific ``label confusion'' layers and co-train the two parts in a parameter-coupled manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria are intuitive and work well in practice. Nonetheless, theoretical understanding of the CCEM criterion has been limited. The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented. Our analysis reveals for the first time that the CCEM can indeed correctly identify the annotators' confusion characteristics and the desired ``ground-truth'' neural classifier under realistic conditions, e.g., when only incomplete annotator labeling and finite samples are available. Second, based on the insights learned from our analysis, two regularized variants of the CCEM are proposed. The regularization terms provably enhance the identifiability of the target model parameters in various more challenging cases. A series of synthetic and real data experiments are presented to showcase the effectiveness of our approach.