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Robust recognition of arbitrary object classes in natural visual scenes is an aspiring goal with numerous practical applications, for instance, in the area of autonomous robotics and autonomous vehicles. One obstacle on the way towards human-like recognition performance is the limitation of computational power, restricting the size of the training and(More)
Convolutional neural networks have achieved good recognition results on image datasets while being computationally efficient, i.e., scaling well with the number of training patterns and the resolution of the patterns. Here we investigate a neural network model that has a similar hierarchical structure, but does not employ weight sharing. Instead, each(More)
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