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arXiv · 2102.06143

Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation

Harris Partaourides·Andreas Voskou·Dimitrios Kosmopoulos·Sotirios Chatzis·Dimitris N. Metaxas
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Abstract

Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance.

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Harris Partaourides, Andreas Voskou, Dimitrios Kosmopoulos, Sotirios Chatzis, Dimitris N. Metaxas. 2021-02-11. Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation. https://arxiv.org/abs/2102.06143

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