  The essence of deep learning is to exploit data to train a deep neural
network (DNN) model. This work explores the reverse process of generating data
from a model, attempting to reveal the relationship between the data and the
model. We repeat the process of Data to Model (DtM) and Data from Model (DfM)
in sequence and explore the loss of feature mapping information by measuring
the accuracy drop on the original validation dataset. We perform this
experiment for both a non-robust and robust origin model. Our results show that
the accuracy drop is limited even after multiple sequences of DtM and DfM,
especially for robust models. The success of this cycling transformation can be
attributed to the shared feature mapping existing in data and model. Using the
same data, we observe that different DtM processes result in models having
different features, especially for different network architecture families,
even though they achieve comparable performance.
