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 261–270 of 308 papers

TitleStatusHype
Medical Image Generation using Generative Adversarial Networks—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge—0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified