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 271–280 of 308 papers

TitleStatusHype
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints—0
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
A CNN toolbox for skin cancer classification—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Efficient Method for Categorize Animals in the WildCode0
Slot Based Image Augmentation System for Object Detection—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified