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 141–150 of 308 papers

TitleStatusHype
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Image augmentation improves few-shot classification performance in plant disease recognition—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Image Augmentation for Satellite Images—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
Game State Learning via Game Scene Augmentation—0
Augment to Detect Anomalies with Continuous Labelling—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
A machine-generated catalogue of Charon's craters and implications for the Kuiper beltCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified