  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.
