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

• URL: http://arxiv.org/abs/2101.09379v1
• Date: Fri, 22 Jan 2021 23:33:11 GMT
• Title: SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
• Authors: Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, and Ulugbek S. Kamilov
• Abstract summary: We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding networks. 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.
• Score: 35.01173046356158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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