Methods and systems for mining minority-class data samples for training a neural network

Abstract

Methods and systems for mining minority-class data samples are described. A minority-class mining service receives activations generated by an inner-layer of a client neural network that has been trained to perform a prediction task that involves classification. The minority-class mining service generates a recalibrated activation using a recalibration neural network, and generates an anomaly detector output using an anomaly detector. From the anomaly detector output, a minority-class score is computed for the data sample represented by a received activation. The computed minority-class score is compared against a minority-class threshold to identify a candidate minority-class data sample. The candidate minority-class data sample can then be labeled and added to the training dataset for the client neural network.