In this study, we consider a crowdsourcing classification problem in which labeling information from crowds is aggregated to infer latent true labels. We propose a fully Bayesian deep generative crowdsourcing model (BayesDGC), which combines the strength of deep neural networks (DNNs) on automatic representation learning and the interpretable probabilistic structure encoding of probabilistic graphical models. The model comprises a DNN classifier as a prior for the true labels and a probabilistic model for the annotation generation process. The DNN classifier and annotation generation process share the latent true label variables. To address the inference challenge, we developed a natural-gradient stochastic variational inference, which combines variational message passing for conjugate parameters and stochastic gradient descent for DNN and learns the distribution of latent true labels and workers’ confusion matrix via end-to-end training. We illustrated the effectiveness of the proposed model using empirical results on 22 real-world datasets.

在这项研究中，我们考虑了一个众包分类问题，其中来自人群的标签信息被汇总以推断潜在的真实标签。我们提出了一个完全的贝叶斯深度生成众包模型（BayesDGC），该模型结合了深度神经网络（DNN）在自动表示学习上的优势以及概率图形模型的可解释概率结构编码。该模型包括DNN分类器（作为真实标签的先验条件）和概率模型（用于注释生成过程）。DNN分类器和注释生成过程共享潜在的真实标签变量。为了解决推理挑战，我们开发了一种自然梯度随机变分推理，它结合了用于共轭参数的变体消息传递和用于DNN的随机梯度下降，并通过端到端培训来学习潜在真实标签和工人混淆矩阵的分布。我们使用对22个现实世界数据集的经验结果说明了所提出模型的有效性。