  Deep neural networks (DNNs) have demonstrated state-of-the-art results on
many pattern recognition tasks, especially vision classification problems.
Understanding the inner workings of such computational brains is both
fascinating basic science that is interesting in its own right - similar to why
we study the human brain - and will enable researchers to further improve DNNs.
One path to understanding how a neural network functions internally is to study
what each of its neurons has learned to detect. One such method is called
activation maximization (AM), which synthesizes an input (e.g. an image) that
highly activates a neuron. Here we dramatically improve the qualitative state
of the art of activation maximization by harnessing a powerful, learned prior:
a deep generator network (DGN). The algorithm (1) generates qualitatively
state-of-the-art synthetic images that look almost real, (2) reveals the
features learned by each neuron in an interpretable way, (3) generalizes well
to new datasets and somewhat well to different network architectures without
requiring the prior to be relearned, and (4) can be considered as a
high-quality generative method (in this case, by generating novel, creative,
interesting, recognizable images).
