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 91–100 of 308 papers

TitleStatusHype
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
Data Augmentation via Levy ProcessesCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Few-Shot Learning for Image Classification of Common FloraCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified