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 201–225 of 308 papers

TitleStatusHype
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual PoliciesCode1
Survey: Image Mixing and Deleting for Data AugmentationCode0
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
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
Salient Objects in ClutterCode1
Few-Shot Learning for Image Classification of Common FloraCode0
InAugment: Improving Classifiers via Internal AugmentationCode1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning ModelsCode1
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
Self-Supervised Pretraining Improves Self-Supervised PretrainingCode1
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
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
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
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified