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 51–60 of 308 papers

TitleStatusHype
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
Semmeldetector: Application of Machine Learning in Commercial Bakeries—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Policy Gradient-Driven Noise MaskCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified