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 41–50 of 308 papers

TitleStatusHype
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
Self-Supervised Pretraining Improves Self-Supervised PretrainingCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Simple Copy-Paste is a Strong Data Augmentation Method for Instance SegmentationCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopyCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Visual Question Generation from Radiology ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified