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

A computing system for identifying a candidate minority-class data sample, the computing system comprising:

• a processing device configured to execute instructions to cause the computing system to:
• receive an activation comprising values of an inner-layer activation representing a given data sample, the received activation being generated by a client neural network that has been trained to perform a classification;
• forward propagate the received activation through a trained recalibration neural network, to generate a recalibrated activation, wherein the trained recalibration neural network has been trained to perform the classification in a manner to avoid overtraining;
• forward propagate the recalibrated activation through a trained anomaly detector, wherein the trained anomaly detector has been trained on activations in which majority-class data samples form a majority;
• compute a minority-class score for the received activation, based on an anomaly detector output;
• identify the given data sample as a candidate minority-class data sample, based on a comparison of the minority-class score against a minority-class threshold;
• and
• communicate an identification of the given data sample as the candidate minority-class data sample.

A computer-readable medium having instructions encoded thereon, wherein the instructions, when executed by a processing device of a computing system, cause the computing system to:

• receive an activation comprising values of an inner-layer activation representing a given data sample, the received activation being generated by a client neural network that has been trained to perform a classification;
• forward propagate the received activation through a trained recalibration neural network, to generate a recalibrated activation, wherein the trained recalibration neural network has been trained to perform the classification in a manner to avoid overtraining;
• forward propagate the recalibrated activation through a trained anomaly detector, wherein the trained anomaly detector has been trained on activations in which majority-class data samples form a majority;
• compute a minority-class score for the received activation, based on an anomaly detector output;
• identify the given data sample as a candidate minority-class data sample, based on a comparison of the minority-class score against a minority-class threshold;
• and
• communicate an identification of the given data sample as the candidate minority-class data sample.