  The two key characteristics of a normalizing flow is that it is invertible
(in particular, dimension preserving) and that it monitors the amount by which
it changes the likelihood of data points as samples are propagated along the
network. Recently, multiple generalizations of normalizing flows have been
introduced that relax these two conditions. On the other hand, neural networks
only perform a forward pass on the input, there is neither a notion of an
inverse of a neural network nor is there one of its likelihood contribution. In
this paper we argue that certain neural network architectures can be enriched
with a stochastic inverse pass and that their likelihood contribution can be
monitored in a way that they fall under the generalized notion of a normalizing
flow mentioned above. We term this enrichment flowification. We prove that
neural networks only containing linear layers, convolutional layers and
invertible activations such as LeakyReLU can be flowified and evaluate them in
the generative setting on image datasets.

Machine Learning;Machine Learning