  In this paper we propose a novel approach for stabilizing the training
process of Generative Adversarial Networks as well as alleviating the mode
collapse problem. The main idea is to introduce a regularization term that we
call intervention loss into the objective. We refer to the resulting generative
model as Intervention Generative Adversarial Networks (IVGAN). By perturbing
the latent representations of real images obtained from an auxiliary encoder
network with Gaussian invariant interventions and penalizing the dissimilarity
of the distributions of the resulting generated images, the intervention loss
provides more informative gradient for the generator, significantly improving
GAN's training stability. We demonstrate the effectiveness and efficiency of
our methods via solid theoretical analysis and thorough evaluation on standard
real-world datasets as well as the stacked MNIST dataset.
