  The ability of the Generative Adversarial Networks (GANs) framework to learn
generative models mapping from simple latent distributions to arbitrarily
complex data distributions has been demonstrated empirically, with compelling
results showing that the latent space of such generators captures semantic
variation in the data distribution. Intuitively, models trained to predict
these semantic latent representations given data may serve as useful feature
representations for auxiliary problems where semantics are relevant. However,
in their existing form, GANs have no means of learning the inverse mapping --
projecting data back into the latent space. We propose Bidirectional Generative
Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and
demonstrate that the resulting learned feature representation is useful for
auxiliary supervised discrimination tasks, competitive with contemporary
approaches to unsupervised and self-supervised feature learning.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Machine Learning