Pseudo Conditional Regularization for Inverse Mapping of GANs

Inverse mapping of the Generative Adversarial Networks (GANs) which projects data to latent space have been recently introduced, and it is shown that the inverse mapping models trained by the bidirectional adversarial learning can enable novel and practical operations including interpolation between real data. However, existing techniques still do not ensure the consistent mapping between the data and their latent representation so that the models are hardly converged throughout training steps. Our discussion begins with empirical investigations on the inconsistency issue of the prior techniques, and we further propose a novel adversarial learning method, Pseudo Conditional Bidirectional GAN (PC-BiGAN), for training the inverse mapping of GANs with a high degree of consistency and similarity-awareness. Our models are specifically guided by the pseudo conditions defined by the proximity relationship among data in unsupervised learned feature space. We demonstrate that our novel bidirectional adversarial learning frameworks improve the performance in sample reconstruction, generation, and interpolation.