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
Hierarchical Attention-based Age Estimation and Bias Estimation—0
High-resolution medical image synthesis using progressively grown generative adversarial networks—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
Image Augmentation Agent for Weakly Supervised Semantic Segmentation—0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
Image Augmentation for Satellite Images—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
Image Augmentations for GAN Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified