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 291–300 of 308 papers

TitleStatusHype
Efficient Augmentation via Data Subsampling—0
Albumentations: fast and flexible image augmentationsCode0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Improved Mixed-Example Data AugmentationCode0
High-resolution medical image synthesis using progressively grown generative adversarial networks—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Parallel Grid Pooling for Data AugmentationCode0
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified