Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceThe performance of a deep neural network can be affected by software and hardware setups when training the network and, therefore, can vary from training to training. This issue of reproducibility, which can be referred to as an “intrinsic” reproducibility of deep learning, can be critical for academic research because reproducibility is a key requirement for journal papers. In this study, we explore this intrinsic reproducibility issue for deep learning-powered parallel imaging reconstruction by using a popular end-to-end variational network. This study may provide minimal requirements for reproducible research in network training.
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