# Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels

## TL;DR

This paper introduces a semi-unsupervised learning framework that combines clustering and deep generative models to effectively classify data when some classes are entirely unlabelled, addressing a realistic scenario in sparse labeling.

## Contribution

It proposes a novel semi-unsupervised learning approach that handles classes with no labelled examples, extending traditional semi-supervised methods.

## Key findings

- Effective learning with half classes unlabelled
- Identifies pitfalls of existing DGM-based semi-supervised methods
- Demonstrates success on multiple datasets

## Abstract

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.