CPC G06F 18/2148 (2023.01) [G06F 18/2155 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01)] | 20 Claims |
1. A method for identifying a candidate minority-class data sample, the method comprising:
receiving 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 propagating 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 propagating 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;
computing a minority-class score for the received activation, based on an anomaly detector output;
identifying 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
communicating an identification of the given data sample as the candidate minority-class data sample.
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