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 110 of 308 papers

TitleStatusHype
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
AutoAugment: Learning Augmentation Policies from DataCode3
Differentiable Data Augmentation with KorniaCode3
A Survey on Data Augmentation in Large Model EraCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
Random Erasing Data AugmentationCode2
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
Deep PCB To COCO ConvertorCode2
Diffusion-Enhanced Test-time Adaptation with Text and Image AugmentationCode2
Show:102550
← PrevPage 1 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified