  Visualizing features in deep neural networks (DNNs) can help understanding
their computations. Many previous studies aimed to visualize the selectivity of
individual units by finding meaningful images that maximize their activation.
However, comparably little attention has been paid to visualizing to what image
transformations units in DNNs are invariant. Here we propose a method to
discover invariances in the responses of hidden layer units of deep neural
networks. Our approach is based on simultaneously searching for a batch of
images that strongly activate a unit while at the same time being as distinct
from each other as possible. We find that even early convolutional layers in
VGG-19 exhibit various forms of response invariance: near-perfect phase
invariance in some units and invariance to local diffeomorphic transformations
in others. At the same time, we uncover representational differences with
ResNet-50 in its corresponding layers. We conclude that invariance
transformations are a major computational component learned by DNNs and we
provide a systematic method to study them.
