By building the model in such a way that assumptions about the data are in the model structure.
ConvNets hardcode translation equivariance, more general convolutions can hardcode more general equivariant structures.
The whole field of geometric deep learning is about constructing nets based on insights about the structure of the data manifold, and many successful nets turn out to do this.
