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 226–250 of 308 papers

TitleStatusHype
Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework—0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Compound Figure Separation of Biomedical Images with Side LossCode0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Survey: Image Mixing and Deleting for Data AugmentationCode0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture—0
Few-Shot Learning for Image Classification of Common FloraCode0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Hierarchical Attention-based Age Estimation and Bias Estimation—0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions—0
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations—0
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
Random Transformation of Image Brightness for Adversarial AttackCode0
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
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
Towards Performance Improvement in Indian Sign Language Recognition—0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified