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 281–290 of 308 papers

TitleStatusHype
Image Augmentation using Radial Transform for Training Deep Neural Networks—0
Image augmentation with conformal mappings for a convolutional neural network—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified