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Deep learning has emerged as a technique of choice for rapid featureextraction across imaging disciplines, allowing rapid conversion of the datastreams to spatial or spatiotemporal arrays of features of interest. However,applications of deep learning in experimental domains are often limited by theout-of-distribution drift between the experiments, where the network trainedfor one set of imaging conditions becomes sub-optimal for different ones. Thislimitation is particularly stringent in the quest to have an automatedexperiment setting, where retraining or transfer learning becomes impracticaldue to the need for human intervention and associated latencies. Here weexplore the reproducibility of deep learning for feature extraction inatom-resolved electron microscopy and introduce workflows based on ensemblelearning and iterative training to greatly improve feature detection. Thisapproach both allows incorporating uncertainty quantification into the deeplearning analysis and also enables rapid automated experimental workflows whereretraining of the network to compensate for out-of-distribution drift due tosubtle change in imaging conditions is substituted for a human operator orprogrammatic selection of networks from the ensemble. This methodology can befurther applied to machine learning workflows in other imaging areas includingoptical and chemical imaging.

Ensemble learning and iterative training (ELIT) machine learning:  applications towards uncertainty quantification and automated experiment in  atom-resolved microscopy