  Machine learning algorithms using deep architectures have been able to
implement increasingly powerful and successful models. However, they also
become increasingly more complex, more difficult to comprehend and easier to
fool. So far, most methods in the literature investigate the decision of the
model for a single given input datum. In this paper, we propose to visualize a
part of the decision function of a deep neural network together with a part of
the data set in two dimensions with discriminative dimensionality reduction.
This enables us to inspect how different properties of the data are treated by
the model, such as outliers, adversaries or poisoned data. Further, the
presented approach is complementary to the mentioned interpretation methods
from the literature and hence might be even more useful in combination with
those. Code is available at https://github.com/LucaHermes/DeepView .
