I'm really interested in using this library, but I have to admit I'm struggling to think of use cases. I can see differentiability being useful in feature extr

I'm really interested in using this library, but I have to admit I'm struggling to think of use cases. I can see differentiability being useful in feature extraction and maybe some uses of augmentation like colour correction, or spatial tranformations -- for example in the image registration example they give -- but overall the kinds of operations offered by the library tend to be used for preprocessing, and not really needed in the trainable pipeline. For instance edge detectors and even most feature detectors are basically hard-coded kernels, are they not? And you wouldn't apply them on intermediate representations, they are only really valid operations for the input layer, and not needed during back-propagation. I find the examples interesting, but mostly are things that could be done using OpenCV and don't exploit the differentiability in any meaningful way. But maybe I am missing something.