3 ms·
I skipped to the table at the end. The gains don't seem enormous. Is there a kind of problem where we would expect quaternions to perform dramatically better th
by Jeff_Brown 9y ago
I skipped to the table at the end. The gains don't seem enormous. Is there a kind of problem where we would expect quaternions to perform dramatically better than other kinds of numbers?
- highd 9y agoNot to mention that it appears they're comparing against networks of the same architecture. If you build your quaternion components with with same number types as your reals you effectively have 4 times the number of parameters, which could be most of the benefit. They should also benchmark against similar architectures with equivalent parameter counts.
- deleted 9y ago[deleted]
- gaudetcj 9y agoHi I'm one of the authors of this paper. Sorry if it unclear, but we reduce the number of filters per layer to account for this. The quaternion networks actually have fewer parameters.
- gaudetcj 9y agoThis paper was mostly to lay out the framework and give out the Keras layers for others to use. We expect that the biggest improvements will come from segmentation where the gains may come from treating each color channel as an imaginary axis. And from architectures like PointNet https://arxiv.org/abs/1612.00593 https://arxiv.org/abs/1612.00593.