• Corpus ID: 2759724

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

@inproceedings{Tarvainen2017MeanTA,
  title={Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results},
  author={Antti Tarvainen and Harri Valpola},
  booktitle={NIPS},
  year={2017}
}
The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. [] Key Method 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…
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References

SHOWING 1-10 OF 48 REFERENCES
Temporal Ensembling for Semi-Supervised Learning
TLDR
Self-ensembling is introduced, where it is shown that this ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training.
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
TLDR
An unsupervised loss function is proposed that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network.
Swapout: Learning an ensemble of deep architectures
TLDR
This work describes Swapout, a new stochastic training method that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFar-100 and proposes a parameterization that reveals connections to exiting architectures and suggests a much richer set of architectures to be explored.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
TLDR
Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Shake-Shake regularization
The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel…
Deep Networks with Stochastic Depth
TLDR
Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation.
Aggregated Residual Transformations for Deep Neural Networks
TLDR
On the ImageNet-1K dataset, it is empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy and is more effective than going deeper or wider when the authors increase the capacity.
Deep Residual Learning for Image Recognition
TLDR
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Variational Autoencoder for Deep Learning of Images, Labels and Captions
TLDR
A novel variational autoencoder is developed to model images, as well as associated labels or captions, and a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone.
On Calibration of Modern Neural Networks
TLDR
It is discovered that modern neural networks, unlike those from a decade ago, are poorly calibrated, and on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
...
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