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 131–140 of 308 papers

TitleStatusHype
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
Handwritten image augmentation—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Attention W-Net: Improved Skip Connections for better Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified