Temporal ensembling for semi-supervised learning
Python
Switch branches/tags
Nothing to show
Clone or download
U-NVIDIA.COM\slaine
U-NVIDIA.COM\slaine second revision
Latest commit a32743a Jan 3, 2017
Permalink
Failed to load latest commit information.
data second revision Jan 3, 2017
LICENSE.md initial commit Nov 4, 2016
README.md second revision Jan 3, 2017
config.py second revision Jan 3, 2017
report.py initial commit Nov 4, 2016
theano_utils.py initial commit Nov 4, 2016
thread_utils.py initial commit Nov 4, 2016
train.py second revision Jan 3, 2017
zca_bn.py initial commit Nov 4, 2016

README.md

Implementation of temporal ensembling and Pi-model. Samuli Laine and Timo Aila, NVIDIA.

Released as part of ICLR 2017 paper submission "Temporal Ensembling for Semi-Supervised Learning".

Additional code (report.py, theano_utils.py, thread_utils.py) by Tero Karras, NVIDIA. Code in zca_bn.py adapted from Tim Salimans' repository at: https://github.com/TimSalimans/weight_norm/blob/master/nn.py

Package versions used when preparing the paper:

  • Theano 0.9.0.dev4
  • Lasagne 0.2.dev1
  • CUDA toolkit 8.0, CUDNN 5105

To configure a training run, edit config.py. To execute, run train.py.