Re: Exploiting Cyclic Symmetry in Convolutional Neural Networks

See also: Oriented Response Networks https://arxiv.org/abs/1701.01833

On Wed, Feb 28, 2018 at 11:40 AM, Jonathan Roy <[email protected]> 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
>
>
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