Improving Generative Adversarial Networks via Adversarial Learning in Latent Space

For Generative Adversarial Networks which map a latent distribution to the target distribution, in this paper, we study how the sampling in latent space can affect the generation performance, especially for images. We observe that, as the neural generator is a continuous function, two close samples in latent space would be mapped into two nearby images, while their quality can differ much as the quality generally does not exhibit a continuous nature in pixel space. From such a continuous mapping function perspective, it is also possible that two distant latent samples can be mapped into two close images (if not exactly the same). In particular, if the latent samples are mapped in aggregation into a single mode, mode collapse occurs. Accordingly, we propose adding an implicit latent transform before the mapping function to improve latent $z$ from its initial distribution, e.g., Gaussian. This is achieved using well-developed adversarial sample mining techniques, e.g. iterative fast gradient sign method (I-FGSM). We further propose new GAN training pipelines to obtain better generative mappings w.r.t quality and diversity by introducing targeted latent transforms into the bi-level optimization of GAN. Experimental results on visual data show that our method can effectively achieve improvement in both quality and diversity.