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 91–100 of 308 papers

TitleStatusHype
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
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 Images—0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
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 Learning—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Handwritten image augmentation—0
Exemplar-Free Continual Transformer with Convolutions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified