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 31–40 of 308 papers

TitleStatusHype
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Data Augmentation for Scene Text RecognitionCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified