  Despite their successful application to a variety of tasks, neural networks
remain limited, like other machine learning methods, by their sensitivity to
shifts in the data: their performance can be severely impacted by differences
in distribution between the data on which they were trained and that on which
they are deployed. In this article, we propose a new family of representations,
called MAGDiff, that we extract from any given neural network classifier and
that allows for efficient covariate data shift detection without the need to
train a new model dedicated to this task. These representations are computed by
comparing the activation graphs of the neural network for samples belonging to
the training distribution and to the target distribution, and yield powerful
data- and task-adapted statistics for the two-sample tests commonly used for
data set shift detection. We demonstrate this empirically by measuring the
statistical powers of two-sample Kolmogorov-Smirnov (KS) tests on several
different data sets and shift types, and showing that our novel representations
induce significant improvements over a state-of-the-art baseline relying on the
network output.
