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 171–180 of 308 papers

TitleStatusHype
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
Diagnosis of COVID-19 based on Chest Radiography—0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Image augmentation with conformal mappings for a convolutional neural network—0
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications—0
Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
Semi-supervised object detection based on single-stage detector for thighbone fracture localization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified