Using synthetic data safely in classification

When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with their true class. Acquiring such training sets is difficult and costly. One way to alleviate this problem is to enlarge training sets by generating artificial, synthetic samples. Of course this immediately raises many questions, perhaps the first being “Why should we trust artificially generated data to be an accurate representative of the real distributions?” Other questions include “When will training on synthetic data work as well as or better than training on real data ?”. We distinguish between sample space (the set of real samples), generator space (all samples that can be generated synthetically), and finally, feature space (the set of samples in terms of finite numerical values). In this paper, we discuss a series of experiments, in which we produced synthetic data in generator space, that is, by convex interpolation among the generating parameters for samples and showed we could amplify real data to produce a classifier that is as accurate as a classifier trained on real data. Specifically, we have explored the feasibility of varying the generating parameters for Knuth’s Metafont system to see if previously unseen fonts could also be recognized.