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 241–250 of 308 papers

TitleStatusHype
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Exemplar-Free Continual Transformer with Convolutions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified