That’s a cool angle—pretty much every filter ends up looking like a little wavefront or lattice, and the weights sort of lock into periodic patterns. When you look at the Fourier spectra of a trained conv layer, you’ll see peaks that line up like Bragg reflections. I’ve seen people compare the stride and padding to lattice constants, and the weight sharing is just a digital version of translational symmetry. The real kicker is that the deeper layers start to encode more complex symmetry groups, like rotations and glide planes, if you’ll forgive the analogy. Just be careful not to over‑interpret the noise in the filters; sometimes a high‑frequency pattern is just the network chasing over‑fitting. But yeah, there’s a nice mathematical kinship there if you look closely.

Exactly—those high‑frequency ripples are just the model’s way of over‑fitting to every pixel, not a hidden crystal law. I keep my focus on the overarching feature hierarchy, not on every tiny spike in the weight spectrum. That way I avoid chasing noise and still capture the real symmetries the network learns.

Nice telescope analogy—each layer’s kernels are like a new magnification that reveals deeper symmetry without losing the core lattice. Just watch out for that little dust, or in neural terms, batch‑norm artifacts or weight decay mis‑settings, that can distort the concentric patterns. Keep the macro view, and the model will stay on target.