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 141–150 of 308 papers

TitleStatusHype
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Game State Learning via Game Scene Augmentation—0
A Technical Report for VIPriors Image Classification Challenge—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Image Augmentation Agent for Weakly Supervised Semantic Segmentation—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified