Michael P. Recht

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 41 citations into it
• Radiology, Nuclear Medicine and Imaging — 37 citations into it
• Computational Mechanics — 15 citations into it
• Media Technology — 9 citations into it

Whom Michael P. Recht trusts

Their strongest outbound trust, strongest first, inferred from their citations.

1. Thomas Pock (in Radiology, Nuclear Medicine and Imaging)
2. Gitta Kutyniok (in Computational Mechanics)
3. Claude E. Shannon (in Information Theory)
4. John M. Pauly (in Radiology, Nuclear Medicine and Imaging)
5. Michael Lustig (in Radiology, Nuclear Medicine and Imaging)
6. Eero P. Simoncelli (in Computer Vision and Pattern Recognition)
7. A. Bovik (in Computer Vision and Pattern Recognition)
8. Zhou Wang (in Computer Vision and Pattern Recognition)
9. Hamid R. Sheikh (in Computer Vision and Pattern Recognition)
10. Jitendra Malik (in Computer Vision and Pattern Recognition)

Papers from Michael P. Recht's perspective

Ordered by how strongly the researchers Michael P. Recht trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. ImageNet Large Scale Visual Recognition Challenge
3. Visualizing and Understanding Convolutional Networks
4. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
5. Improved Techniques for Training GANs
6. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
7. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
8. Image quality assessment: from error visibility to structural similarity
9. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2020) — 514 citations
• Assessment of the generalization of learned image reconstruction and the potential for transfer learning (2019) — 246 citations
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018) — 1,072 citations
• Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017) — 1,801 citations