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For example if you have a segmentation or regression problem, but the features that you are interested in are always in the same or similar place in the input image or time series (across different training/test samples), then does it some how make more sense to use a locally connected input layer rather than a convolutional layer because the features that you want to learn will be specific to that location, enabling a shallower network with fewer parameters ?

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