  Deep learning has emerged as a technique of choice for rapid feature
extraction across imaging disciplines, allowing rapid conversion of the data
streams to spatial or spatiotemporal arrays of features of interest. However,
applications of deep learning in experimental domains are often limited by the
out-of-distribution drift between the experiments, where the network trained
for one set of imaging conditions becomes sub-optimal for different ones. This
limitation is particularly stringent in the quest to have an automated
experiment setting, where retraining or transfer learning becomes impractical
due to the need for human intervention and associated latencies. Here we
explore the reproducibility of deep learning for feature extraction in
atom-resolved electron microscopy and introduce workflows based on ensemble
learning and iterative training to greatly improve feature detection. This
approach both allows incorporating uncertainty quantification into the deep
learning analysis and also enables rapid automated experimental workflows where
retraining of the network to compensate for out-of-distribution drift due to
subtle change in imaging conditions is substituted for a human operator or
programmatic selection of networks from the ensemble. This methodology can be
further applied to machine learning workflows in other imaging areas including
optical and chemical imaging.
