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arXiv · 1912.05539

Deep One-bit Compressive Autoencoding

Shahin Khobahi·Arindam Bose·Mojtaba Soltanalian
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Abstract

Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In this paper, we consider the design of a one-bit compressive autoencoder, and propose a novel hybrid model-based and data-driven methodology that allows us to not only design the sensing matrix for one-bit data acquisition, but also allows for learning the latent-parameters of an iterative optimization algorithm specifically designed for the problem of one-bit sparse signal recovery. Our results demonstrate a significant improvement compared to state-of-the-art model-based algorithms.

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BibTeXRIS

Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian. 2019-12-10. Deep One-bit Compressive Autoencoding. https://arxiv.org/abs/1912.05539

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