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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Semi-supervised Learning from Crowds Using Deep Generative Models
| Title: | Semi-supervised Learning from Crowds Using Deep Generative Models |
| Authors: | Atarashi, Kyohei Browse this author | | Oyama, Satoshi Browse this author →KAKEN DB | | Kurihara, Masahito Browse this author →KAKEN DB |
| Issue Date: | 2018 |
| Publisher: | AAAI |
| Journal Title: | Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) |
| Abstract: | Although supervised learning requires a labeled dataset, ob- taining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the field of machine learning as a way to collect labels at rela- tively low cost. However, the labels obtained by crowdsourc- ing, i.e., from non-expert workers, are often noisy. A num- ber of methods have thus been devised for inferring true la- bels, and several methods have been proposed for learning classifiers directly from crowdsourced labels, referred to as learning from crowds. A more practical problem is learn- ing from crowdsourced labeled data and unlabeled data, i.e., semi-supervised learning from crowds. This paper presents a novel generative model of the labeling process in crowdsourc- ing. It leverages unlabeled data effectively by introducing latent featuresand a data distribution. Because the data distri- bution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can be regarded as a kind of deep generative model. The problems caused by the intractability of latent variable posteriors is solved by intro- ducing an inference model. The experiments show that it out- performs four existing models, including a baseline model, on the MNIST dataset with simulated workers and the Rot- ten Tomatoes movie review dataset with Amazon Mechanical Turk workers. |
| Conference Name: | AAAI Conference on Artificial Intelligence |
| Conference Sequence: | 32 |
| Conference Place: | New Orleans, Louisiana |
| Type: | proceedings (author version) |
| URI: | http://hdl.handle.net/2115/67695 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小山 聡
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