Synthetic data for neural network training using vectors

A computer-implemented method for machine learning comprising:

• obtaining a facial expression training dataset for a first neural network implemented on a machine learning system;
• identifying one or more sparse classes of facial expressions within the training dataset;
• generating synthetic data to augment the sparse classes using at least one generative adversarial network (GAN), wherein a feature selector selects values from one or more vector representations, the values being used as an input to the GAN;
• augmenting the training dataset using the synthetic data from the at least one GAN based on the selected values of the feature selector;
• and
• training the machine learning system, using the training dataset that was augmented.

A computer system for machine learning comprising:

• a memory which stores instructions;
• one or more processors attached to the memory wherein the one or more processors, when executing the instructions which are stored, are configured to:
• obtain facial images for a neural network training dataset;
• encode facial elements from the facial images into one or more vector representations of the facial elements using a machine learning neural network;
• train a generative adversarial network (GAN) generator to provide synthetic vectors based on the one or more vector representations, wherein a feature selector selects values from the one or more vector representations, the values being used as an input to the GAN, and wherein the synthetic vectors enable avoidance of discriminator detection in the GAN;
• generate additional synthetic vectors in the GAN, wherein the additional synthetic vectors avoid discriminator detection;
• and
• train the machine learning neural network, using the additional synthetic vectors that were generated by the GAN based on the selected values of the feature selector.