Convolution layers are understood to encode an infinitely strong prior on spatial locality. (Ian Goodfellow’s Deep Learning book, summary here https://medium.com/inveterate-learner/deep-learning-book-chapter-9-convolutional-networks-45e43bfc718d). From my understanding, this means that conv kernels are good at capturing regional patterns (e.g. a point here, a stroke there, etc).

Do strided transpose convolutions encode a prior of some kind as well? (an uneducated guess from my end – a preference for texture and pattern at a larger scale?)