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Unsupervised Data Augmentation

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Apr 30 2019 Quoc Le

Data augmentation is often associated with supervised learning. We find *unsupervised* data augmentation works better. It combines well with transfer learning (e.g. BERT) and improves everything when datasets have a small number of labeled examples. Link: http://arxiv.org/abs/1904.12848
3 replies, 676 likes


Apr 30 2019 Thang Luong

Introducing UDA, our new work on "Unsupervised data augmentation" for semi-supervised learning (SSL) with Qizhe Xie, Zihang Dai, Eduard Hovy, & @quocleix. SOTA results on IMDB (with just 20 labeled examples!), SSL Cifar10 & SVHN (30% error reduction)! https://arxiv.org/abs/1904.12848 https://t.co/rBf2U9NQL0
3 replies, 451 likes


May 18 2019 Quoc Le

Links to the mentioned papers. MixMatch: https://arxiv.org/abs/1905.02249 Unsupervised Data Augmentation: https://arxiv.org/abs/1904.12848
1 replies, 82 likes


May 19 2019 Quoc Le

To add to Vincent's point above, new findings also include: 1. The method is general (works well for images & texts). 2. The method works well on top of transfer learning (e.g., BERT). You can find these results in Unsupervised Data Augmentation paper: https://arxiv.org/abs/1904.12848
0 replies, 66 likes


May 06 2019 Mihail Eric

Yum! Unsupervised data augmentation that works from @GoogleAI @QizheXie @quocleix. New state-of-the-art on various language and vision tasks: https://arxiv.org/pdf/1904.12848.pdf
0 replies, 54 likes


Jul 11 2019 Thang Luong

These plots (also included in the updated version of our UDA paper https://arxiv.org/abs/1904.12848 with a lot more results & details) illustrate very well Vincent's article on the quiet revolution of semi-supervised learning! https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c
2 replies, 8 likes


Apr 30 2019 Arjun (Raj) Manrai

Wow: "on IMDb, UDA with 20 labeled examples outperforms the state-of-the-art model trained on 1250x more labeled data" https://arxiv.org/abs/1904.12848
0 replies, 8 likes


Sep 29 2019 arXiv CS-CV

Unsupervised Data Augmentation for Consistency Training http://arxiv.org/abs/1904.12848
0 replies, 7 likes


May 06 2019 Daisuke Okanohara

In semi-supervised learning, VAT adds adversarial noise to unsupervised data and makes its prediction distribution matches the original distribution. UDA instead applies data augmentation methods and gradually increases the signal from the supervised data https://arxiv.org/abs/1904.12848
0 replies, 5 likes


Jul 11 2019 arXiv CS-CL

Unsupervised Data Augmentation for Consistency Training http://arxiv.org/abs/1904.12848
0 replies, 4 likes


Jul 23 2019 BioDecoded

Advancing Semi-supervised Learning with Unsupervised Data Augmentation | Google AI Blog https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html https://arxiv.org/abs/1904.12848 #MachineLearning https://t.co/qzMNH0r4eq
0 replies, 3 likes


Oct 24 2019 BioDecoded

Inference of clonal selection in cancer populations using single-cell sequencing data | Bioinformatics https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html https://arxiv.org/pdf/1904.12848.pdf #MachineLearning https://t.co/AWt9hBaOGr
1 replies, 3 likes


Jun 27 2019 Quoc Le

@ivan_bezdomny In NLP, there is backtranslation method that works quite well as a data augmentation method. You can check out its use in UDA: https://arxiv.org/abs/1904.12848 Link to code: https://github.com/google-research/uda#run-back-translation-data-augmentation-for-your-dataset
1 replies, 3 likes


Sep 28 2019 arXiv CS-CV

Unsupervised Data Augmentation for Consistency Training http://arxiv.org/abs/1904.12848
0 replies, 2 likes


May 01 2019 Saleh Elmohamed

Really nice work by Quoc Le and colleagues at Google & CMU on unsupervised data augmentation. Highly recommend checking out their latest paper at the arXiv.
0 replies, 1 likes


May 08 2019 AK

#UDA or unsupervised data augmentations new technique from @google to generate synthetic data for #neuralnetworks #AI #machinelearning https://arxiv.org/pdf/1904.12848.pdf
0 replies, 1 likes


Oct 10 2019 Cherrypick

UNSUPERVISED DATA AUGMENTATION (UDA) https://arxiv.org/pdf/1904.12848.pdf https://youtu.be/cqjcJ7XqGkA trying to use for stock market sentiment analysis (pytorch and BERT)
1 replies, 0 likes


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