See also: Oriented Response Networks https://arxiv.org/abs/1701.01833
On Wed, Feb 28, 2018 at 11:40 AM, Jonathan Roy <jonr...@gmail.com> wrote: > I'm curious if anyone has applied this idea in their Go software, and what > results you obtained? It is a way to make rotations (and transpositions > with more effort) go away as an issue, regardless of the way you input the > board you'd get the same result back out. Short summary from the paper ( > https://arxiv.org/pdf/1602.02660.pdf): > > We have introduced a framework for building rotation > equivariant neural networks, using four new layers which > can easily be inserted into existing network architectures. > Beyond adapting the minibatch size used for training, no > further modifications are required. We demonstrated improved > performance of the resulting equivariant networks > on datasets which exhibit full rotational symmetry, while > reducing the number of parameters. A fast GPU implementation > of the rolling operation for Theano (using > CUDA kernels) is available at https://github.com/benanne/kaggle-ndsb. > > It was apparently used by this science competition winner: > > http://benanne.github.io/2015/03/17/plankton.html > > And there's related codebase here that implements the paper Group > Equivariant Convolutional Networks (https://tacocohen.files. > wordpress.com/2016/06/gcnn.pdf). > > https://github.com/tscohen/gconv_experiments > > The paper makes it sound like implementing for rotation would be straight > forward, and implementing for transposition more difficult but also > doable.Which sounds perfect for Go AI applications. > > -Jonathan > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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