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 101–110 of 308 papers

TitleStatusHype
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Learning to Compose Domain-Specific Transformations for Data AugmentationCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified