# Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop

arXiv: https://arxiv.org/abs/1912.05098 · HTML: https://mlsanity.com/paper/1912.05098/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.12215/) (2022)
- [Memory-Efficient Network for Large-scale Video Compressive Sensing](https://mlsanity.com/paper/2103.03089/) (2021)
- [Online Deep Equilibrium Learning for Regularization by Denoising](https://mlsanity.com/paper/2205.13051/) (2022)
- [SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees](https://mlsanity.com/paper/2101.09379/) (2021)
- [Memory-efficient model-based deep learning with convergence and robustness guarantees](https://mlsanity.com/paper/2206.04797/) (2022)
- [Memory-efficient Learning for High-Dimensional MRI Reconstruction](https://mlsanity.com/paper/2103.04003/) (2021)
- [Learned Half-Quadratic Splitting Network for MR Image Reconstruction](https://mlsanity.com/paper/2112.09760/) (2021)
- [Self-Supervised Learning from Noisy and Incomplete Data](https://mlsanity.com/paper/2601.03244/) (2026)
- [Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers](https://mlsanity.com/paper/2205.00428/) (2022)
- [Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2201.07562/) (2022)
- [Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms](https://mlsanity.com/paper/2205.03677/) (2022)
- [GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.08393/) (2022)
- [Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks](https://mlsanity.com/paper/2306.02886/) (2023)
- [Computationally Efficient 3D MRI Reconstruction with Adaptive MLP](https://mlsanity.com/paper/2301.08868/) (2023)
- [Computationally Efficient Information-Driven Optical Design with Interchanging Optimization](https://mlsanity.com/paper/2507.07789/) (2025)
- [Efficient Unrolled Networks for Large-Scale 3D Inverse Problems](https://mlsanity.com/paper/2601.02141/) (2026)
- [Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)](https://mlsanity.com/paper/2304.01351/) (2023)

## Abstract (arXiv)

Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks). However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical 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. We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.