E. D. Cubuk

Publications100
h-index41
Citations10,152
Highly Influential Citations1,404
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SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
TLDR
This work presents SpecAugment, a simple data augmentation method for speech recognition that is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients) and achieves state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work.
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
TLDR
This paper demonstrates the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling, and shows that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks.
Randaugment: Practical automated data augmentation with a reduced search space
TLDR
This work proposes a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.
AutoAugment: Learning Augmentation Policies from Data
TLDR
This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data).
AutoAugment: Learning Augmentation Strategies From Data
TLDR
This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data).
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
TLDR
AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
TLDR
This work creates a unified reimplemention and evaluation platform of various widely-used SSL techniques and finds that the performance of simple baselines which do not use unlabeled data is often underreported, that SSL methods differ in sensitivity to the amount of labeled and unlabeling data, and that performance can degrade substantially when the unlabelED dataset contains out-of-class examples.
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
TLDR
A variant of AutoAugment which learns the augmentation policy while the model is being trained, and is significantly more data-efficient than prior work, requiring between $5\times and $16\times less data to reach the same accuracy.
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
TLDR
A variant of AutoAugment which learns the augmentation policy while the model is being trained, and is significantly more data-efficient than prior work, requiring between 5 times and 16 times less data to reach the same accuracy.
RandAugment: Practical data augmentation with no separate search
TLDR
RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous learned augmentation approaches on CIFAR-10, CIFar-100, SVHN, and ImageNet.
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