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In convolutional deep neural networks, receptive field (RF) size increaseswith hierarchical depth. When RF size approaches full coverage of the inputimage, different RF positions result in RFs with different specificity, asportions of the RF fall out of the input space. This leads to a departure fromthe convolutional concept of positional invariance and opens the possibilityfor complex forms of context specificity.

Node Specificity in Convolutional Deep Nets Depends on Receptive Field Position and Size