FURI | Summer 2020

A Benchmarking Framework for Data-Driven Compressive Sensing

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The objective of the research is to develop a benchmarking framework with a unified API to allow researchers to quantitatively evaluate new compressive sensing algorithms.To achieve fair and extensive comparisons,all data-driven methods are implemented in PyTorch,and are compared to state-of-the-art model-based methods with a variety of dataset and parameter setups.The CSGAN algorithm was reimplemented in PyTorch as part of this framework,based on an existing TensorFlow implementation.However,it was found that the original implementation of the research paper has been missing key components,and further investigation is required to reproduce the results from the original CSGAN research paper.

Student researcher

Márk Lakatos-Tóth

Computer science

Hometown: Budapest, Pest megye, Hungary

Graduation date: Fall 2020