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 181–190 of 308 papers

TitleStatusHype
A CNN toolbox for skin cancer classification—0
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
A convolutional neural network of low complexity for tumor anomaly detection—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified