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We conduct an empirical study to test the ability of Convolutional NeuralNetworks (CNNs) to reduce the effects of nuisance transformations of the inputdata, such as location, scale and aspect ratio. We isolate factors by adoptinga common convolutional architecture either deployed globally on the image tocompute class posterior distributions, or restricted locally to compute classconditional distributions given location, scale and aspect ratios of boundingboxes determined by proposal heuristics. In theory, averaging the latter shouldyield inferior performance compared to proper marginalization. Yet empiricalevidence suggests the converse, leading us to conclude that - at the currentlevel of complexity of convolutional architectures and scale of the data setsused to train them - CNNs are not very effective at marginalizing nuisancevariability. We also quantify the effects of context on the overallclassification task and its impact on the performance of CNNs, and proposeimproved sampling techniques for heuristic proposal schemes that improveend-to-end performance to state-of-the-art levels. We test our hypothesis on aclassification task using the ImageNet Challenge benchmark and on awide-baseline matching task using the Oxford and Fischer's datasets.

An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability