On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2020
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| _version_ | 1866913957104582656 |
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| author | Yu, Bing Sun, Ke Wang, He Lin, Zhouchen Zhu, Zhanxing |
| author_facet | Yu, Bing Sun, Ke Wang, He Lin, Zhouchen Zhu, Zhanxing |
| contents | The scarcity of class-labeled data is a ubiquitous bottleneck in many machine learning problems. While abundant unlabeled data typically exist and provide a potential solution, it is highly challenging to exploit them. In this paper, we address this problem by leveraging Positive-Unlabeled~(PU) classification and the conditional generation with extra unlabeled data \emph{simultaneously}. We present a novel training framework to jointly target both PU classification and conditional generation when exposed to extra data, especially out-of-distribution unlabeled data, by exploring the interplay between them: 1) enhancing the performance of PU classifiers with the assistance of a novel Classifier-Noise-Invariant Conditional GAN~(CNI-CGAN) that is robust to noisy labels, 2) leveraging extra data with predicted labels from a PU classifier to help the generation. Theoretically, we prove the optimal condition of CNI-CGAN and experimentally, we conducted extensive evaluations on diverse datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_07841 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation Yu, Bing Sun, Ke Wang, He Lin, Zhouchen Zhu, Zhanxing Machine Learning The scarcity of class-labeled data is a ubiquitous bottleneck in many machine learning problems. While abundant unlabeled data typically exist and provide a potential solution, it is highly challenging to exploit them. In this paper, we address this problem by leveraging Positive-Unlabeled~(PU) classification and the conditional generation with extra unlabeled data \emph{simultaneously}. We present a novel training framework to jointly target both PU classification and conditional generation when exposed to extra data, especially out-of-distribution unlabeled data, by exploring the interplay between them: 1) enhancing the performance of PU classifiers with the assistance of a novel Classifier-Noise-Invariant Conditional GAN~(CNI-CGAN) that is robust to noisy labels, 2) leveraging extra data with predicted labels from a PU classifier to help the generation. Theoretically, we prove the optimal condition of CNI-CGAN and experimentally, we conducted extensive evaluations on diverse datasets. |
| title | On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2006.07841 |