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

Showing 5160 of 308 papers

TitleStatusHype
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network PerformanceCode1
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
Implicit Semantic Data Augmentation for Deep NetworksCode1
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Fast AutoAugmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified