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We can better understand deep neural networks by identifying which featureseach of their neurons have learned to detect. To do so, researchers havecreated Deep Visualization techniques including activation maximization, whichsynthetically generates inputs (e.g. images) that maximally activate eachneuron. A limitation of current techniques is that they assume each neurondetects 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 astorefront. Previous activation maximization techniques constructed imageswithout regard for the multiple different facets of a neuron, creatinginappropriate mixes of colors, parts of objects, scales, orientations, etc.Here, we introduce an algorithm that explicitly uncovers the multiple facets ofeach neuron by producing a synthetic visualization of each of the types ofimages that activate a neuron. We also introduce regularization methods thatproduce state-of-the-art results in terms of the interpretability of imagesobtained by activation maximization. By separately synthesizing each type ofimage a neuron fires in response to, the visualizations have more appropriatecolors and coherent global structure. Multifaceted feature visualization thusprovides a clearer and more comprehensive description of the role of eachneuron.

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks