Data Augmentation Generative Adversarial Networks

The potential of GANs do anonymize sensitive data (such as medical records) using the generator of a GAN trained on the raw data has been effectively demonstrated in the past, the authors of this paper take a similar approach to generate data for augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalize it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data.


Want to receive more content like this in your inbox?