Semi-Supervised Learning with Normalizing FlowsDownload PDF

25 Sept 2019 (modified: 22 Oct 2023)ICLR 2020 Conference Blind SubmissionReaders: Everyone
TL;DR: Probabilistic semi-supervised learning method based on normalizing flows
Abstract: We propose Flow Gaussian Mixture Model (FlowGMM), a general-purpose method for semi-supervised learning based on a simple and principled probabilistic framework. We approximate the joint distribution of the labeled and unlabeled data with a flexible mixture model implemented as a Gaussian mixture transformed by a normalizing flow. We train the model by maximizing the exact joint likelihood of the labeled and unlabeled data. We evaluate FlowGMM on a wide range of semi-supervised classification problems across different data types: AG-News and Yahoo Answers text data, MNIST, SVHN and CIFAR-10 image classification problems as well as tabular UCI datasets. FlowGMM achieves promising results on image classification problems and outperforms the competing methods on other types of data. FlowGMM learns an interpretable latent repesentation space and allows hyper-parameter free feature visualization at real time rates. Finally, we show that FlowGMM can be calibrated to produce meaningful uncertainty estimates for its predictions.
Keywords: Semi-Supervised Learning, Normalizing Flows
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