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

Showing 111120 of 308 papers

TitleStatusHype
Realistic Data Enrichment for Robust Image Segmentation in Histopathology0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Progressive Random Convolutions for Single Domain Generalization0
Improving the Transferability of Adversarial Samples by Path-Augmented MethodCode1
Bias mitigation techniques in image classification: fair machine learning in human heritage collections0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Towards Composable Distributions of Latent Space Augmentations0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified