Memory-efficient Learning for Large-scale Computational Imaging

• URL: http://arxiv.org/abs/2003.05551v1
• Date: Wed, 11 Mar 2020 23:08:04 GMT
• Title: Memory-efficient Learning for Large-scale Computational Imaging
• Authors: Michael Kellman, Kevin Zhang, Jon Tamir, Emrah Bostan, Michael Lustig, Laura Waller
• Abstract summary: We propose a memory-efficient learning procedure that exploits the reversibility of the network's layers to enable data-driven design for large-scale imaging systems. We demonstrate our method on a small-scale compressed sensing example, as well as two large-scale real-world systems: multi-channel magnetic resonance imaging and super-resolution optical microscopy.
• Score: 3.255705667028885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based networks). However, for real-world large-scale inverse problems, computing gradients via backpropagation is infeasible due to memory limitations of graphics processing units. In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network's layers to enable data-driven design for large-scale computational imaging systems. We demonstrate our method on a small-scale compressed sensing example, as well as two large-scale real-world systems: multi-channel magnetic resonance imaging and super-resolution optical microscopy.

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