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

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Showing 231–240 of 308 papers

TitleStatusHype
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
Image Augmentation for Satellite Images—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
Image Augmentations for GAN Training—0
Image Augmentation using Radial Transform for Training Deep Neural Networks—0
Image augmentation with conformal mappings for a convolutional neural network—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified