TRAINING NEURAL NETWORK CLASSIFIERS USING CLASSIFICATION METADATA FROM OTHER ML CLASSIFIERS

摘要

Techniques for training a neural network classifier using classification metadata from another, non-neural network (non-NN) classifier are provided. In one set of embodiments, a computer system can train the non-NN classifier using a training data set, where the training results in a trained version of the non-NN network classifier. The computer system can further classify a data instance in the plurality of data instances using the trained non-NN classifier, the classifying generating a first class distribution for the data instance, and provide the data instance's feature set as input to a neural network classifier, the providing causing the neural network classifier to generate a second class distribution for the data instance. The computer system can then compute a loss value indicating a degree of divergence between the first and second class distributions and provide the loss value as feedback to the neural network classifier, which can cause the neural network classifier to adjust one or more internal edge weights in an manner that reduces the degree of divergence.