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Group equivariant CNN (G-CNN) #2246
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That would be super helpful for all people working with physical systems :) . A small note:
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All this seems like a great feature set to prototype in a dedicated library! The general criteria we have for inclusion in core Flux is maturity (of both methods and code), ubiquity and cross-domain applicability. Flux also needs to be very careful about factors such as backwards compatibility and architectural fit, so a separate package should offer both more agility and room to experiment with design. |
Group convolution is one of the member of geometric deep learning. Should be considered supported by geometric deep learning library based on Flux.jl. GeometricFlux.jl is a good place to support group convolutions and this model is listed at FluxML/GeometricFlux.jl#225. PRs are welcome. |
Thanks for pointing it out @yuehhua, that's indeed what we are after. |
Motivation and description
Group equivariant CNN (G-CNN) embed rotation invariance or (and) scale invariance on top of translation invariance in CNNs.
Some references:
Is there support for these type of architectures in Flux? In PyTorch, an implementation on top of the main library is exemplified here, even though I am not sure if this is directly implemented nowadays.
Thanks!
Possible Implementation
No response
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