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Proceedings of the Python in Science Conferences
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. We experimentally evaluate the atomic benefits to the perform-ing DML on the VAE latent space such as the enhanced ability to train using unlabelled data and to induce bias given prior knowledge. We find that jointly training DML with an autoencoder and VAE may be potentiallydoi:10.25080/majora-212e5952-022 fatcat:t2n7pjwkw5buxiou4mcywejegi