Open-Book Testing and Multi-Label Deep Generative Models
Rastin Rastgoufard, Abdul Rahman Alsamman · 2018
Deep Generative Models (DGMs) are very powerful semi-supervised classifiers. We aim to further improve their prediction accuracies by constructing novel generative models that incorporate multiple labels and by proposing open-book testing, a new testing paradigm that leverages the semi-supervised nature of DGMs. We perform all of our experiments on the NORB data set. Open-book testing allows unlabeled test data to be used while training in an effort to combat overfitting. We show experimentally that open-book testing significantly increases classification performance even though no label information is provided. Further, we develop five new multi-label DGMs. One is a generic multi-label model and four are custom-tailored to the NORB data set. We find that, compared to a single-label classifier, the presence of additional labels degrades performance despite open-book testing but is nearly perfect at 99.7% when a priori independence is enforced.