Search Results for author: Jure Zbontar

Found 6 papers, 6 papers with code

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

15 code implementations4 Mar 2021 Jure Zbontar, Li Jing, Ishan Misra, Yann Lecun, Stéphane Deny

This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors.

General Classification Object Detection +3

Implicit Rank-Minimizing Autoencoder

3 code implementations NeurIPS 2020 Li Jing, Jure Zbontar, Yann Lecun

An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited.

Image Generation Representation Learning +1

End-to-End Variational Networks for Accelerated MRI Reconstruction

3 code implementations14 Apr 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia Johnson

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).

MRI Reconstruction

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

1 code implementation6 Jan 2020 Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht

Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Image Reconstruction

GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

1 code implementation CVPR 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson

In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors.

MRI Reconstruction

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