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Selecting appropriate datasets is critical in modern computer vision.However, no general-purpose tools exist to evaluate the extent to which twodatasets differ. For this, we propose representing images - and by extensiondatasets - using Distributions of Neuron Activations (DNAs). DNAs fitdistributions, such as histograms or Gaussians, to activations of neurons in apre-trained feature extractor through which we pass the image(s) to represent.This extractor is frozen for all datasets, and we rely on its generallyexpressive power in feature space. By comparing two DNAs, we can evaluate theextent to which two datasets differ with granular control over the comparisonattributes of interest, providing the ability to customise the way distancesare measured to suit the requirements of the task at hand. Furthermore, DNAsare compact, representing datasets of any size with less than 15 megabytes. Wedemonstrate the value of DNAs by evaluating their applicability on severaltasks, including conditional dataset comparison, synthetic image evaluation,and transfer learning, and across diverse datasets, ranging from synthetic catimages to celebrity faces and urban driving scenes.

Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations