Training network to maximize true positive rate at low false positive rate

Abstract

Some embodiments provide a method for training a machine-trained (MT) network that processes input data using network parameters. The method maps a set of input instances to a set of output values by propagating the set of input instances through the MT network. The set of input instances includes input instances for each of multiple categories. For a particular input instance selected as an anchor instance, the method calculates a true positive rate (TPR) for the MT network as a function of a distance between the output value for the anchor instance and the output value for each input instance not in a same category as the anchor instance. The method calculates a loss function for the anchor instance that maximizes the TPR for the MT network at low false positive rate. The method trains the network parameters using the calculated loss function.