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 1120 of 308 papers

TitleStatusHype
Diffusion-Enhanced Test-time Adaptation with Text and Image AugmentationCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
Data Augmentation for Scene Text RecognitionCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified