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arXiv · 2101.09379

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

Jiaming Liu·Yu Sun·Weijie Gan·Xiaojian Xu·Brendt Wohlberg·Ulugbek S. Kamilov
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Abstract

Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our numerical results on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the batch network at a fraction of training and testing complexity.

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Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov. 2021-01-22. SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees. https://doi.org/10.1109/tci.2021.3085534

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