2020

GridMask Data Augmentation

Chen, Pengguang, Liu, Shu, Zhao, Hengshuang et al.

Understand

We propose a novel data augmentation method `GridMask' in this paper.

It utilizes information removal to achieve state-of-the-art results in a variety of computer vision tasks.
We analyze the requirement of information dropping.
Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and yet very effective.

Built on

Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Adaptive dropout for training deep neural networks
Lei Jimmy Ba and Brendan J. Frey · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
Matthew D. Zeiler and Rob Fergus · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2015
Earlier work this paper cites.
Fast R-CNN
Ross B. Girshick · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2015
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Then

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mixup: Beyond empirical risk minimization
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