Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data

• URL: http://arxiv.org/abs/2006.07841v2
• Date: Fri, 9 Feb 2024 02:23:03 GMT
• Title: Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data
• Authors: Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu
• Abstract summary: The scarcity of class-labeled data is a ubiquitous bottleneck in many machine learning problems. We address this problem by leveraging Positive-Unlabeled(PU) classification and the conditional generation with extra unlabeled data. We present a novel training framework to jointly target both PU classification and conditional generation when exposed to extra data.
• Score: 77.31213472792088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}. In particular, 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, verifying the simultaneous improvements in both classification and generation.

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