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 21–30 of 308 papers

TitleStatusHype
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Data Augmentation for Scene Text RecognitionCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified