Abstract
Deep metric learning has been demonstrated to be highly effective in learning
semantic representation and encoding information that can be used to measure
data similarity, by relying on the embedding learned from metric learning. At
the same time, variational autoencoder (VAE) has widely been used to
approximate inference and proved to have a good performance for directed
probabilistic models. However, for traditional VAE, the data label or feature
information are intractable. Similarly, traditional representation learning
approaches fail to represent many salient aspects of the data. In this project,
we propose a novel integrated framework to learn latent embedding in VAE by
incorporating deep metric learning. The features are learned by optimizing a
triplet loss on the mean vectors of VAE in conjunction with standard evidence
lower bound (ELBO) of VAE. This approach, which we call Triplet based
Variational Autoencoder (TVAE), allows us to capture more fine-grained
information in the latent embedding. Our model is tested on MNIST data set and
achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma &
Welling, 2013) achieves triplet accuracy of 75.08%.
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