  Maximum likelihood (ML) and adversarial learning are two popular approaches
for training generative models, and from many perspectives these techniques are
complementary. ML learning encourages the capture of all data modes, and it is
typically characterized by stable training. However, ML learning tends to
distribute probability mass diffusely over the data space, $e.g.$, yielding
blurry synthetic images. Adversarial learning is well known to synthesize
highly realistic natural images, despite practical challenges like mode
dropping and delicate training. We propose an $\alpha$-Bridge to unify the
advantages of ML and adversarial learning, enabling the smooth transfer from
one to the other via the $\alpha$-divergence. We reveal that generalizations of
the $\alpha$-Bridge are closely related to approaches developed recently to
regularize adversarial learning, providing insights into that prior work, and
further understanding of why the $\alpha$-Bridge performs well in practice.
