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HexaConv
[article]
2018
arXiv
pre-print
The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convolutions. However, for reasons of performance and ease of implementation, it has been necessary to limit the group convolution to transformations that can be applied to the filters
arXiv:1803.02108v1
fatcat:eyb4bblayjeyjporwhoidcgt4q