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Advancing Semi-supervised Learning with Unsupervised Data Augmentation
Wednesday, July 10, 2019
Posted by Qizhe Xie, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team
Success in deep learning has largely been enabled by key factors such as algorithmic advancements, parallel processing hardware (
GPU
/
TPU
), and the availability of large-scale labeled datasets, like
ImageNet
. However, when labeled data is scarce, it can be difficult to train neural networks to perform well. In this case, one can apply data augmentation methods, e.g., paraphrasing a sentence or rotating an image, to effectively increase the amount of labeled training data. Recently, there has been significant progress in the design of data augmentation approaches for a variety of areas such as
natural language processing
(NLP),
vision
, and
speech
. Unfortunately, data augmentation is often limited to
supervised learning
only, in which labels are required to transfer from original examples to augmented ones.
Example augmentation operations for text-based (
top
) or image-based (
bottom
) training data.
In our recent work, “
Unsupervised Data Augmentation (UDA) for Consistency Training
”, we demonstrate that one can also perform data augmentation on unlabeled data to significantly improve
semi-supervised learning
(SSL). Our results support the recent
revival of semi-supervised learning
, showing that: (1) SSL can match and even outperform purely supervised learning that uses orders of magnitude more labeled data, (2) SSL works well across domains in both text and vision and (3) SSL combines well with transfer learning, e.g., when fine-tuning from
BERT
. We have also open-sourced our code (
github
) for the community to replicate and build upon.
Unsupervised Data Augmentation Explained
Unsupervised Data Augmentation (UDA) makes use of both labeled data and unlabeled data. To use labeled data, it computes the
loss function
using standard methods for supervised learning to train the model, as shown in the left part of the graph below. For unlabeled data, consistency training is applied to enforce the predictions to be similar for an unlabeled example and the augmented unlabeled example, as shown in the right part of the graph. Here, the same model is applied to both the unlabeled example and its augmented counterpart to produce two model predictions, from which a consistency loss is computed (i.e., the distance between the two prediction distributions). UDA then computes the final loss by jointly optimizing both the supervised loss from the labeled data and the unsupervised consistency loss from the unlabeled data.
An overview of Unsupervised Data Augmentation (UDA).
Left:
Standard supervised loss is computed when labeled data is available.
Right:
With unlabeled data, a consistency loss is computed between an example and its augmented version.
By minimizing the consistency loss, UDA allows for label information to propagate smoothly from labeled examples to unlabeled ones. Intuitively, one can think of UDA as an implicit iterative process. First, the model relies on a small amount of labeled examples to make correct predictions for some unlabeled examples, from which the label information is propagated to augmented counterparts through the consistency loss. Over time, more and more unlabeled examples will be predicted correctly which reflects the improved generalization of the model. Various other types of noise have been tested for consistency training (e.g.,
Gaussian noise
,
adversarial noise
, and others), yet we found that data augmentation outperforms all of them in many settings, leading to state-of-the-art performance on a wide variety of tasks from language to vision. UDA applies different existing augmentation methods depending on the task at hand, including
back translation
,
AutoAugment
, and
TF-IDF
word replacement.
Benchmarks in NLP and Computer Vision
UDA is surprisingly effective in the low-data regime. With only 20 labeled examples, UDA achieves an error rate of 4.20 on the
IMDb
sentiment analysis task by leveraging 50,000 unlabeled examples. This result outperforms the previous state-of-the-art model trained on 25,000 labeled examples with an error rate of 4.32. In the large-data regime, with the full training set, UDA also provides robust gains.
Benchmark on IMDb, a
sentiment analysis
task. UDA surpasses state-of-the-art results in supervised learning across different training sizes.
On the
CIFAR-10
semi-supervised learning benchmark, UDA outperforms all existing SSL methods, such as
VAT
and
ICT
by significant margins. With 4k examples, UDA achieves an error rate of 5.27, matching the performance of the fully supervised model that uses 50k examples. Furthermore, with a more advanced architecture,
PyramidNet+ShakeDrop
, UDA achieves a new state-of-the-art error rate of 2.7, a more than 45% reduction in error rate compared to the previous best semi-supervised result. On
SVHN
, UDA achieves an error rate of 2.46 with only 1k labeled examples, matching the performance of the fully supervised model trained with ~70k labeled examples.
SSL benchmark on CIFAR-10 and SVHN image classification tasks. UDA surpases existing semi-supervised learning methods.
On
ImageNet
with 10% labeled examples, UDA improves the top-1 (top-5) accuracy from 55.1% (77.3%) with the supervised baseline and no unlabeled examples to 68.7% (88.5%) using all images from ImageNet as unlabeled examples. In the high-data regime with the fully labeled set and 1.3M extra unlabeled examples, UDA continues to provide gains from 78.3% (94.4%) to 79.0% (94.5%).
Release
We have released the
codebase
of UDA, together with all data augmentation methods, e.g., back-translation with pre-trained translation models, to replicate our results. We hope that this release will further advance the progress in semi-supervised learning.
Acknowledgements
Special thanks to the co-authors of the paper Zihang Dai, Eduard Hovy, and Quoc V. Le. We’d also like to thank Hieu Pham, Adams Wei Yu, Zhilin Yang, Colin Raffel, Olga Wichrowska, Ekin Dogus Cubuk, Guokun Lai, Jiateng Xie, Yulun Du, Trieu Trinh, Ran Zhao, Ola Spyra, Brandon Yang, Daiyi Peng, Andrew Dai, Samy Bengio and Jeff Dean for their help with this project. A
preprint
is available online.
In addition, we would also like to refer readers to other approaches on semi-supervised learning concurrently developed by our colleagues in Google Research, such as MixMatch and S4L, that have demonstrated promising results. We thank the authors of the
MixMatch
and
S4L
papers for their comments and feedback on our paper and blog post.
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