Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications. Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing ( Image credit: Kornia )
data-augmentation-2
Image Augmentation
3260 papers • 126 benchmarks • 313 datasets
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Intel Image Classification
Intel Image Classification
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Improved Regularization of Convolutional Neural Networks with Cutout
Graham W. Taylor, Terrance Devries•Mon Aug 14 2017
This paper shows that the simple regularization technique of randomly masking out square regions of input during training, which is called cutout, can be used to improve the robustness and overall performance of convolutional neural networks.
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