Abstract
When is it safe to use synthetic training 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), parameter 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 parameter 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. We also varied parameters for an image quality model. We have found that training on interpolated data is for the most part safe, that is to say never produced more errors. Furthermore, the classifier trained on interpolated data often improved class accuracy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.