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 251–300 of 308 papers

TitleStatusHype
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network PerformanceCode1
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Implicit Semantic Data Augmentation for Deep NetworksCode1
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints—0
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
A CNN toolbox for skin cancer classification—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Efficient Method for Categorize Animals in the WildCode0
Slot Based Image Augmentation System for Object Detection—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Learning Data Augmentation Strategies for Object DetectionCode1
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Fast AutoAugmentCode1
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Unsupervised Data Augmentation for Consistency TrainingCode1
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
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Improved Mixed-Example Data AugmentationCode0
AutoAugment: Learning Augmentation Policies from DataCode3
High-resolution medical image synthesis using progressively grown generative adversarial networks—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Parallel Grid Pooling for Data AugmentationCode0
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified