Awesome
PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences:
- The raw image is first convolved, to obtain #
channels
feature maps. - The upsampling is followed by a convolution, and the result is then summed with the other inputs. In the paper, they first sum and then convolve on the result.
- These can be easily changed in the
UpSample
,DownSample
,SameRes
class definitions insideneural_fabrics.py
. Feel free to implement your own procedure and experiment.
To run on CIFAR-10:
<pre> python neural_fabric.py --dataset cifar10 --save fabric_cifar10 </pre>Test set error: 7.2%, with rotation and translation augmented training data.
<img src="./img/fabric.png">