Advancing beyond Interpretability and explainability approaches that may uncover what a Deep Neural Network (DNN) models, i.e., what each node (cell) in the network represents and what images are most likely to activate the model provide a mimicked type of learning of generalization applicable to previously unseen samples. The approach provides an ability to detect and circumvent adversarial attacks, with self-verification and trust-building structural modeling. Computing systems may now define what it means to learn in deep networks, and how to use this knowledge for a multitude of practical applications.
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