Reviews: Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference

The author(s) extend the idea of regularizing classifiers to be invariant to the tangent space of the learned manifold of the data to use GAN based architectures. This is a worthwhile idea to revisit as significant advances have been made in generative modeling in the intervening time since the last major paper in the area, the CAE was published. Crucial to the idea is the existence of an encoder learning an inverse mapping of the standard generator of GAN training. This is still an area of active research in the GAN literature that as of yet has no completely satisfactory approach. As current inference techniques for GANs are still quite poor, the authors propose two improvements to one technique, BiGAN, which are worthwhile contributions. 1) They adopt the feature matching loss proposed in "Improved techniques for training gans" and 2) they augment the BiGAN objective with another term that evaluates how the generator maps the inferred latent code for a given real example.