GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Last update: Feb 15, 2022

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GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

This is the code to the paper: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery by:

Jonathan Sauder (EPFL)

Martin Genzel (Utrecht University)

Peter Jung (TU Berlin)

An example for how to use this codebase for the Single-Pixel Imaging experiments from the paper is provided in main_singlepixel.py.

Please write us an email in case of questions.

Requirements: torch, torchvision, scipy, numpy, matplotlib, pandas, pywt

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GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Related tags

Overview

GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Owner

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Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network"

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