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 161–170 of 308 papers

TitleStatusHype
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
TorMentor: Deterministic dynamic-path, data augmentations with fractalsCode1
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer SensorsCode0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms—0
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Show:102550
← PrevPage 17 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified