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 151–160 of 308 papers

TitleStatusHype
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Masked Autoencoders are Robust Data AugmentorsCode1
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
Deep PCB To COCO ConvertorCode2
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified