LAUSR

metric and its derivatives can enable similar approaches in the DNN context. I often tell students, when first starting to learn about research, that they should keep an eye out for the papers in an area that everyone else claims to have beaten: Those are the papers that stimulated other researchers. DeepXplore will be such a paper. Its specific metrics and constraints on example generation are unlikely to be the final word in DNN testing, but the work that follows will exist because of researchers seeing these ideas and trying to improve upon them. The core framework from DeepXplore will likely endure: Establish an effective coverage metric based upon the numerical values obtained by the activations of the neural network and use a constrained search procedure to maximize coverage with respect to that metric.