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 71–80 of 308 papers

TitleStatusHype
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
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
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Augmentor: An Image Augmentation Library for Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified