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
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
FitVid: Overfitting in Pixel-Level Video PredictionCode1
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified