mlsanityData to 14 Oct 2026

Invert to Learn to Invert

arXiv 1911.10914 ↗First posted 25 Nov 2019cs.LG stat.MLcited by 18

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Abstract (arXiv)

Iterative learning to infer approaches have become popular solvers for inverse problems. However, their memory requirements during training grow linearly with model depth, limiting in practice model expressiveness. In this work, we propose an iterative inverse model with constant memory that relies on invertible networks to avoid storing intermediate activations. As a result, the proposed approach allows us to train models with 400 layers on 3D volumes in an MRI image reconstruction task. In experiments on a public data set, we demonstrate that these deeper, and thus more expressive, networks perform state-of-the-art image reconstruction.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 5; 2024: 2; 2025: 0; 2026: 1.

18 papers.

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  1. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  2. 2023Reversible Vision Transformerscited by 33
  3. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  4. 2022Stable Deep MRI Reconstruction using Generative Priorscited by 21
  5. 2021Preconditioned training of normalizing flows for variational inference in inverse problemscited by 20
  6. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 20
  7. 2021Deep Bayesian inference for seismic imaging with taskscited by 12
  8. 2021Learning by example: fast reliability-aware seismic imaging with normalizing flowscited by 10
  9. 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
  10. 2022Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solvercited by 4
  11. 2022GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstructioncited by 3
  12. 2023Refining Amortized Posterior Approximations using Gradient-Based Summary Statisticscited by 3
6 more
  1. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  2. 2024Complexity of Injectivity and Verification of ReLU Neural Networkscited by 2
  3. 2023On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosiscited by 1
  4. 2024On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstructioncited by 1
  5. 2023A statistical perspective on algorithm unrolling models for inverse problemscited by 0
  6. 2026Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotescited by 0