  We can better understand deep neural networks by identifying which features
each of their neurons have learned to detect. To do so, researchers have
created Deep Visualization techniques including activation maximization, which
synthetically generates inputs (e.g. images) that maximally activate each
neuron. A limitation of current techniques is that they assume each neuron
detects only one type of feature, but we know that neurons can be multifaceted,
in that they fire in response to many different types of features: for example,
a grocery store class neuron must activate either for rows of produce or for a
storefront. Previous activation maximization techniques constructed images
without regard for the multiple different facets of a neuron, creating
inappropriate mixes of colors, parts of objects, scales, orientations, etc.
Here, we introduce an algorithm that explicitly uncovers the multiple facets of
each neuron by producing a synthetic visualization of each of the types of
images that activate a neuron. We also introduce regularization methods that
produce state-of-the-art results in terms of the interpretability of images
obtained by activation maximization. By separately synthesizing each type of
image a neuron fires in response to, the visualizations have more appropriate
colors and coherent global structure. Multifaceted feature visualization thus
provides a clearer and more comprehensive description of the role of each
neuron.
