PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

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Deep Learning deep-learning pytorch
Overview

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 inside neural_fabrics.py. Feel free to implement your own procedure and experiment.

To run on CIFAR-10:

python neural_fabric.py --dataset cifar10 --save fabric_cifar10

Test set error: 7.2%, with rotation and translation augmented training data.

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Comments
  • 3D structure is not implemented

    3D structure is not implemented

    It seems that the paper claimed a 3D structure, but this project just implemented 2D. And it's very difficult to train it, I'm a little puzzled about what to do next. Looking forward to your reply! Thanks image

    opened by Chen-yu-Zheng 0
Owner
Anuvabh Dutt
Anuvabh Dutt
GitHub
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