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

Showing 3140 of 308 papers

TitleStatusHype
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in VideoCode1
Data Augmentation for Scene Text RecognitionCode1
FitVid: Overfitting in Pixel-Level Video PredictionCode1
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual PoliciesCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Salient Objects in ClutterCode1
InAugment: Improving Classifiers via Internal AugmentationCode1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning ModelsCode1
Show:102550
← PrevPage 4 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified