Title: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,
Abstract: The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change only once per epoch, Temporal Ensembling becomes unwieldy
when learning large datasets. To overcome this problem, we propose Mean
Teacher, a method that averages model weights instead of label predictions. As
an additional benefit, Mean Teacher improves test accuracy and enables training
with fewer labels than Temporal Ensembling. Without changing the network
architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
labels, outperforming Temporal Ensembling trained with 1000 labels. We also
show that a good network architecture is crucial to performance. Combining Mean
Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
from 35.24% to 9.11%.