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

Showing 8190 of 308 papers

TitleStatusHype
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Augmentor: An Image Augmentation Library for Machine LearningCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified