SOTAVerified

Image Augmentation

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 )

Papers

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Showing 301–308 of 308 papers

TitleStatusHype
Learning to Compose Domain-Specific Transformations for Data AugmentationCode0
Random Erasing Data AugmentationCode2
Improved Regularization of Convolutional Neural Networks with CutoutCode1
Image Augmentation using Radial Transform for Training Deep Neural Networks—0
Augmentor: An Image Augmentation Library for Machine LearningCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
Data Augmentation via Levy ProcessesCode0
Three things everyone should know to improve object retrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified