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 81–90 of 308 papers

TitleStatusHype
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Efficient Augmentation via Data Subsampling—0
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts—0
Enhancing weed detection performance by means of GenAI-based image augmentation—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 an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified