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 6170 of 308 papers

TitleStatusHype
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified