Generating synthetic data examples as interpolation of two data examples that is linear in the space of relative scores

A computer system comprising:

• one or more processor cores;
• and
• a memory that stores computer instructions that when executed by the one or more processor cores causes the one or more processor cores to:
• train, through machine learning, a data generator with one or more training objectives, wherein the one or more training objectives comprise a first objective to produce a data example for a specific value of R, where R is value related to S1(x) and S2(x), where, for a data example, x, generated by the data generator, S1(x) is a likelihood that the data example x is in a first class of a first selected data example and S2(x) is a likelihood that the data example x is in a second class of a second selected data example, wherein S1(x) and S2(x) are determined by a discriminator that is trained through machine learning to discriminate between the first and second classes;
• after training the data generator, generate with the data generator a synthetic data example for each of multiple specific values of R, such that the data generator generates multiple synthetic data examples;
• and
• after generating the multiple synthetic data examples:
• determine parameters for a curve fitting the multiple synthetic data examples generated by the data generator for the multiple specific values of R;
• generate additional synthetic data examples along the curve;
• classify, by a classifier of the computer, the additional synthetic data examples;
• and
• modify a training hyperparameter of the generator based on a consistency of classifications by the classifier of the additional synthetic data examples.