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 191–200 of 308 papers

TitleStatusHype
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
A machine-generated catalogue of Charon's craters and implications for the Kuiper beltCode0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified