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 221–230 of 308 papers

TitleStatusHype
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays—0
deepTerra -- AI Land Classification Made Easy—0
Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
Design of Arabic Sign Language Recognition Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified