just because an image is 2-D doesn’t mean that the model can’t use higher dimensional representations in subsequent layers. For an image, you could imagine a n

just because an image is 2-D doesn’t mean that the model can’t use higher dimensional representations in subsequent layers. For an image, you could imagine a network learning to push the image through a filter bank that does oriented local frequency decomposition and turns it into 4D {height}x{width}x{spatial freq}X{orientation} before dealing with color channels or image batches