A VAE extended with a classifier head and a combined reconstruction-plus-classification loss improves semi-supervised classification and, for most classes, anomaly detection on MNIST, Fashion-MNIST, and UCI-HAR.
Semi-unsupervised Learning of Human Activity using Deep Generative Models
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abstract
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use as many real-world datasets are like this. Here we demonstrate a new deep generative model for classification in this regime. Our model, a Gaussian mixture deep generative model, demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.
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cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Augmenting Variational Autoencoders with Sparse Labels: A Unified Framework for Unsupervised, Semi-(un)supervised, and Supervised Learning
A VAE extended with a classifier head and a combined reconstruction-plus-classification loss improves semi-supervised classification and, for most classes, anomaly detection on MNIST, Fashion-MNIST, and UCI-HAR.