Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels
This addresses a practical issue in real-world datasets with biased labeling, though it appears incremental as it builds on existing semi-supervised and clustering methods.
The paper tackles the problem of semi-unsupervised learning, where some classes have no labeled examples in training, by combining clustering and semi-supervised learning with deep generative models, showing effective learning when half the classes are unlabeled and the other half sparsely labeled.
In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the case that some classes of data are found only in the unlabelled dataset -- perhaps the labelling process was biased -- so we do not have any labelled examples to train on for some classes. We call this learning regime $\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning, where some classes have no labelled exemplars in the training set. First, we outline the pitfalls associated with trying to apply deep generative model (DGM)-based semi-supervised learning algorithms to datasets of this type. We then show how a combination of clustering and semi-supervised learning, using DGMs, can be brought to bear on this problem. We study several different datasets, showing how one can still learn effectively when half of the ground truth classes are entirely unlabelled and the other half are sparsely labelled.