Web: http://arxiv.org/abs/2205.05587

May 12, 2022, 1:11 a.m. | Sean C. Epstein, Timothy J. P. Bray, Margaret Hall-Craggs, Hui Zhang

cs.LG updates on arXiv.org arxiv.org

Deep learning (DL) is gaining popularity as a parameter estimation method for
quantitative MRI. A range of competing implementations have been proposed,
relying on either supervised or self-supervised learning. Self-supervised
approaches, sometimes referred to as unsupervised, have been loosely based on
auto-encoders, whereas supervised methods have, to date, been trained on
groundtruth labels. These two learning paradigms have been shown to have
distinct strengths. Notably, self-supervised approaches have offered lower-bias
parameter estimates than their supervised alternatives. This result is
counterintuitive …

arxiv deep deep learning learning physics training

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