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 141–150 of 308 papers

TitleStatusHype
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified