An attempt to generate new bridge types from latent space of generative
  flow
Hongjun Zhang
Through examples of coordinate and probability transformation between
different distributions, the basic principle of normalizing flow is introduced
in a simple and concise manner. From the perspective of the distribution of
random variable function, the essence of probability transformation is
explained, and the scaling factor Jacobian determinant of probability
transformation is introduced. Treating the dataset as a sample from the
population, obtaining normalizing flow is essentially through sampling surveys
to statistically infer the numerical features of the population, and then the
loss function is established by using the maximum likelihood estimation method.
This article introduces how normalizing flow cleverly solves the two major
application challenges of high-dimensional matrix determinant calculation and
neural network reversible transformation. Using symmetric structured image
dataset of three-span beam bridge, arch bridge, cable-stayed bridge and
suspension bridge, constructing and training normalizing flow based on the Glow
API in the TensorFlow Probability library. The model can smoothly transform the
complex distribution of the bridge dataset into a standard normal distribution,
and from the obtained latent space sampling, it can generate new bridge types
that are different from the training dataset.