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 181–190 of 308 papers

TitleStatusHype
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in VideoCode1
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
Attention W-Net: Improved Skip Connections for better Representations—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
FitVid: High-Capacity Pixel-Level Video Prediction—0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified