  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 $NC(x \cdot a, a, r)$ that leverages adversarial training to match
each conditional distribution $P(X_r|X_a=x_a)$. After training, the NC
generalizes to sample from 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.
