Image Augmentation
101 papers with code • 1 benchmarks • 1 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Image Augmentation
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Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Most implemented papers
Adversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks
Although we can benefit a lot from DA, designing appropriate DA policies requires a lot of expert experience and time consumption, and the evaluation of searching the optimal policies is costly.
Efficient Method for Categorize Animals in the Wild
Walleclipse/iWildCam_2019_FGVC6
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Thanks to advanced regularization strategies and ensemble learning, we got top 7/336 places in the final leaderboard.
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
Image augmentation is a widely used technique to improve the performance of convolutional neural networks (CNNs).
Adversarial Policy Gradient for Deep Learning Image Augmentation
victorychain/Adversarial-Policy-Gradient-Augmentation
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The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset.
Implicit Semantic Data Augmentation for Deep Networks
blackfeather-wang/ISDA-for-Deep-Networks
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NeurIPS 2019
Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., adding sunglasses or changing backgrounds.
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
We also compared our method with other data augmentation techniques.
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance
mahmoudnafifi/WB_color_augmenter
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ICCV 2019
There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results.
Can AI help in screening Viral and COVID-19 pneumonia?
The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation.
