[D] Any-dimensional equivariant neural networks

I found this paper very interesting. We kind of make same assumptions, that the authors are making, while using covnet for computer vision. I was wondering can we extend for computer vision use cases


Abstract
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Traditional supervised learning aims to learn an unknown mapping by fitting a function to a set of input-output pairs with a fixed dimension. The fitted function is then defined on inputs of the same dimension. However, in many settings, the unknown mapping takes inputs …

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