Data Augmentation Powered by Generative Adversarial Networks
Károly Bence Póka, Márton Szemenyei · 2020
Face identification projects must be based on a highquality database of the faces to be recognized. The accuracy of the identification depends on the illumination and the diversity of the subject's expression, among other things. Naturally, a more diverse training database and data augmentation can help in loss reduction. In this research, we attempt to increase the quality of few-shot learning face identification by using Generative Adversarial Network-based data augmentation techniques. This paper presents a novel method to embed images into GAN's latent space, and to use the augmented versions for few-shot learning.