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 121–130 of 308 papers

TitleStatusHype
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
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
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
A CNN toolbox for skin cancer classification—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified