TRAINING A STUDENT NEURAL NETWORK TO MIMIC A MENTOR NEURAL NETWORK WITH INPUTS THAT MAXIMIZE STUDENT-TO-MENTOR DISAGREEMENT

摘要

A method and system is provided for training a new neural network to mimic a target neural network without access to the target neural network or its original training dataset. The target neural network and the new neural network may be probed with input data to generate corresponding target and new output data. Input data may be detected that generates a maximum or above threshold difference between the corresponding target and new output data. A divergent probe training dataset may be generated comprising the input data that generates the maximum or above threshold difference and the corresponding target output data. The new neural network may be trained using the divergent probe training dataset to generate the target output data. The new neural network may be iteratively trained using an updated divergent probe training dataset dynamically adjusted as the new neural network changes during training.