DEEP NEURAL NETWORK TRAINING DATA GENERATION

Disclosed implementations improve data that is used to train a deep neural network to determine a device type or a device operational state at a location based on voltage data at the location. For example, labeled training data for a location may be up-sampled to increase the amount of labeled training data that is available for use in training the DNN by obtaining labeled data from other known locations that are determined to be similar to the location. Likewise, the up-sampled training data may be subsampled during training to remove potentially noisy data from the training data, thereby improving the accuracy of the trained DNN.