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
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
Outline-Guided Object Inpainting with Diffusion Models—0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Polarimetric image augmentation—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Progressive Random Convolutions for Single Domain Generalization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified