| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Due to the large amount of labeled data required for deep learning methods, crowdsourcing has emerged as a convenient and cost-efficient way of obtaining training data. To deal with the noisy nature of the collected labels, many algorithms have been proposed to infer true labels and learn a classifier from the obtained noisy labels. The algorithms are often challenged by the occurrence of expert bias and data bias. Specifically, each expert may have diverse backgrounds and abilities, causing them to make specific types of mistakes, and each example from a given data may have features that systematically shift the majority opinion of experts. We propose a model based on deep learning that simultaneously infers the expert bias, the data bias, and learns a classifier. We empirically show the effectiveness of our model on both simulated and real world datasets under different scenarios.
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