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Deep neural networks (DNNs) have demonstrated state-of-the-art results onmany pattern recognition tasks, especially vision classification problems.Understanding the inner workings of such computational brains is bothfascinating basic science that is interesting in its own right - similar to whywe study the human brain - and will enable researchers to further improve DNNs.One path to understanding how a neural network functions internally is to studywhat each of its neurons has learned to detect. One such method is calledactivation maximization (AM), which synthesizes an input (e.g. an image) thathighly activates a neuron. Here we dramatically improve the qualitative stateof the art of activation maximization by harnessing a powerful, learned prior:a deep generator network (DGN). The algorithm (1) generates qualitativelystate-of-the-art synthetic images that look almost real, (2) reveals thefeatures learned by each neuron in an interpretable way, (3) generalizes wellto new datasets and somewhat well to different network architectures withoutrequiring the prior to be relearned, and (4) can be considered as ahigh-quality generative method (in this case, by generating novel, creative,interesting, recognizable images).

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks