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 91–100 of 308 papers

TitleStatusHype
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Augment to Detect Anomalies with Continuous Labelling—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Design of Arabic Sign Language Recognition Model—0
DiffClass: Diffusion-Based Class Incremental Learning—0
Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
deepTerra -- AI Land Classification Made Easy—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified