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
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Diagnosis of COVID-19 based on Chest Radiography—0
DiffClass: Diffusion-Based Class Incremental Learning—0
Diffusion Models for Robotic Manipulation: A Survey—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Efficient Augmentation via Data Subsampling—0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified