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 91100 of 308 papers

TitleStatusHype
Randomize to Generalize: Domain Randomization for Runway FOD Detection0
Selective Volume Mixup for Video Action RecognitionCode0
Domain Generalization with Fourier Transform and Soft ThresholdingCode0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies0
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalizationCode0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation0
Handwritten image augmentation0
Exemplar-Free Continual Transformer with Convolutions0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified