dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
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Abstract (arXiv)
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2019.
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Citing papers per year: 2022: 3; 2023: 2.
5 papers.
- 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13
- 2022A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstructioncited by 6
- 2022On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapycited by 3
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitzcited by 2
- 2023Multi-scale MRI reconstruction via dilated ensemble networkscited by 0
- 2023Multi-scale MRI reconstruction via dilated ensemble networks7 Oct 2023
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz12 May 2023
- 2022A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction5 Jul 2022
- 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning14 Jun 2022
- 2022On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy10 Feb 2022