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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 281–290 of 308 papers

TitleStatusHype
DenseNet Models for Tiny ImageNet ClassificationCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Yelp Food Identification via Image Feature Extraction and Classification—0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Efficient Augmentation via Data Subsampling—0
Albumentations: fast and flexible image augmentationsCode0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
Show:102550
← PrevPage 29 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified