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 251–260 of 308 papers

TitleStatusHype
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
Face Mask Detection using Transfer Learning of InceptionV3—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Few-shot target-driven instance detection based on open-vocabulary object detection models—0
Fiducial Focus Augmentation for Facial Landmark Detection—0
Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture—0
FitVid: High-Capacity Pixel-Level Video Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified