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 221–230 of 308 papers

TitleStatusHype
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
Random Transformation of Image Brightness for Adversarial AttackCode0
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE—0
Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Sparse Signal Models for Data Augmentation in Deep Learning ATRCode0
Simple Copy-Paste is a Strong Data Augmentation Method for Instance SegmentationCode1
Towards Performance Improvement in Indian Sign Language Recognition—0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Differentiable Data Augmentation with KorniaCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified