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 131–140 of 308 papers

TitleStatusHype
Parallel Grid Pooling for Data AugmentationCode0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Exemplar-Free Continual Transformer with Convolutions—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified