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 261–270 of 308 papers

TitleStatusHype
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration—0
FusiformNet: Extracting Discriminative Facial Features on Different Levels—0
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints—0
Game State Learning via Game Scene Augmentation—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
Handwritten image augmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified