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 3140 of 308 papers

TitleStatusHype
Fast AutoAugmentCode1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Data Augmentation for Scene Text RecognitionCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified