  Semi-supervised learning methods using Generative Adversarial Networks (GANs)
have shown promising empirical success recently. Most of these methods use a
shared discriminator/classifier which discriminates real examples from fake
while also predicting the class label. Motivated by the ability of the GANs
generator to capture the data manifold well, we propose to estimate the tangent
space to the data manifold using GANs and employ it to inject invariances into
the classifier. In the process, we propose enhancements over existing methods
for learning the inverse mapping (i.e., the encoder) which greatly improves in
terms of semantic similarity of the reconstructed sample with the input sample.
We observe considerable empirical gains in semi-supervised learning over
baselines, particularly in the cases when the number of labeled examples is
low. We also provide insights into how fake examples influence the
semi-supervised learning procedure.
