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We introduce 'semi-unsupervised learning', a problem regime related totransfer learning and zero-shot learning where, in the training data, someclasses are sparsely labelled and others entirely unlabelled. Models able tolearn from training data of this type are potentially of great use as manyreal-world datasets are like this. Here we demonstrate a new deep generativemodel for classification in this regime. Our model, a Gaussian mixture deepgenerative model, demonstrates superior semi-unsupervised classificationperformance on MNIST to model M2 from Kingma and Welling (2014). We apply themodel to human accelerometer data, performing activity classification andstructure discovery on windows of time series data.

Semi-unsupervised Learning of Human Activity using Deep Generative Models