AUTOMATED SELECTION OF UNANNOTATED DATA FOR ANNOTATION BASED ON FEATURES GENERATED DURING TRAINING

An example system includes a processor to train a neural network model using annotated training data to generate features. The processor is to select a feature vector of the neural network model. The processor is to execute an inference stage on the annotated training data via the neural network model to generate a first set of values corresponding to the annotated training data for features in the selected feature vector. The processor is to execute the inference stage on unannotated data to generate a second set of values corresponding to the unannotated data for the features in the selected feature vector. The processor is to select an item in unannotated data that matches an uncovered combination of feature values in the annotated training data. The processor is to send the selected item for annotation and receive a corresponding additional annotated item to be added to the annotated training data.