Deep learning (DL) has emerged as a new tool for solving ill-posed image reconstruction problems and generated a lot of interest in the MRI community. However, image learning is a very high-dimensional problem and deep networks, if not trained properly, would have instability problems. Building upon a recent analysis, we present a further analysis of the instability problems, highlighting: a) the overfitting problem due to limited training data, b) inaccurate density estimation, and c) inadequate sampling from a probability density function. We also present a theoretical analysis of the prediction error based on statistical learning theory.
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