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 271–280 of 308 papers

TitleStatusHype
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
Show:102550
← PrevPage 28 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified