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 211–220 of 308 papers

TitleStatusHype
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
CIMON: Towards High-quality Hash Codes—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified