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 301308 of 308 papers

TitleStatusHype
Compound Figure Separation of Biomedical Images with Side LossCode0
Survey: Image Mixing and Deleting for Data AugmentationCode0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Selective Volume Mixup for Video Action RecognitionCode0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Albumentations: fast and flexible image augmentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified