  Generative Adversarial Networks can learn the mapping of random noise to
realistic images in a semi-supervised framework. This mapping ability can be
used for semi-supervised image classification to detect images of an unknown
class where there is no training data to be used for supervised classification.
However, if the unknown class shares similar characteristics to the known
class(es), GANs can learn to generalize and generate images that look like both
classes. This generalization ability can hinder the classification performance.
In this work, we propose the Vanishing Twin GAN. By training a weak GAN and
using its generated output image parallel to the regular GAN, the Vanishing
Twin training improves semi-supervised image classification where image
similarity can hurt classification tasks.
