Learning about an exponential amount of conditional distributions.

We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector X. The NC is a function NC(x . a, a, r) that leverages adversarial training to match each conditional distribution P(X-r vertical bar X-a = x(a)). After training, the NC generalizes to sample conditional distributions never seen, including the joint distribution. The NC is also able to auto-encode examples, providing data representations useful for downstream classification tasks. In sum, the NC integrates different self-supervised tasks (each being the estimation of a conditional distribution) and levels of supervision (partially observed data) seamlessly into a single learning experience.