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 281–290 of 308 papers

TitleStatusHype
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Yelp Food Identification via Image Feature Extraction and Classification—0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified