Overcoming Distribution Shifts in Plug-and-Play Methods with Test- Time Training-Reference-Cited by-同舟云学术

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Overcoming Distribution Shifts in Plug-and-Play Methods with Test- Time Training

Published:2023-12-10 Issue: Volume:119 Page:186-190
ISSN:
Container-title:2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
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Author:

Chandler Edward P.¹,Shoushtari Shirin²,Liu Jiaming²,Asif M. Salman³,Kamilov Ulugbek S.¹

Affiliation:

1. Washington University in St. Louis,Department of Computer Science & Engineering,MO

2. Washington University in St. Louis,Department of Electrical & Systems Engineering,MO

3. University of California,Department of Electrical & Computer Engineering,Riverside,CA

Funder

Gordon and Betty Moore Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10402605/10403414/10403502.pdf?arnumber=10403502

Reference22 articles.

1. Convolutional Neural Networks for Inverse Problems in Imaging: A Review

2. Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods

3. Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

4. Plug-and-Play priors for model based reconstruction

5. Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

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