Variational Autoencoders For Semi-Supervised Deep Metric Learning

Nathan Safir, Meekail Zain, Curtis Godwin, Eric Miller, Bella Humphrey, Shannon P. Quinn

Abstract: Deep metric learning (DML) methods generally do not incorporate unlabelled data. We propose borrowing components of the variational autoencoder (VAE) methodology to extend DML methods to train on semi-supervised datasets.