Semi-supervised learning with an open augmenting unknown class for cost-effective training and reliable classifications

The ability to (a) train off partially labelled datasets and (b) ensure resulting networks separate data outside the domain of interest hugely expands the practical and cost-effective applicability of neural network classifiers. We design a classifier based off generative adversarial networks (GANs) that trains off a practical and cost-saving semi-supervised criteria which, specifically, allows novel classes within the unlabelled training set. Furthermore, we ensure the resulting classifier is capable of absolute novel class detection, be these from the semi-supervised unlabelled training set or a so-called open set. Results are both state-of-the-art and a first of its kind. We argue this technique greatly decreases training cost in respect to labelling while greatly improving the reliability of classifications.