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 276–300 of 308 papers

TitleStatusHype
Slot Based Image Augmentation System for Object Detection—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
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
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
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
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified