A survey on Image Data Augmentation for Deep Learning

@article{Shorten2019ASO,
  title={A survey on Image Data Augmentation for Deep Learning},
  author={Connor Shorten and Taghi M. Khoshgoftaar},
  journal={Journal of Big Data},
  year={2019},
  volume={6},
  pages={1-48}
}
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem… 
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References

SHOWING 1-10 OF 139 REFERENCES

Data augmentation for improving deep learning in image classification problem

This paper has compared and analyzed multiple methods of data augmentation in the task of image classification, starting from classical image transformations like rotating, cropping, zooming, histogram based methods and finishing at Style Transfer and Generative Adversarial Networks, along with the representative examples.

Research on data augmentation for image classification based on convolution neural networks

Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

A new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image and achieves a new state-of-the-art test error of 2.19% on CIFAR-10.

Chest x-ray generation and data augmentation for cardiovascular abnormality classification

This work investigates the use of GANs for producing chest X-ray images to augment a dataset and uses this dataset to train a convolutional neural network to classify images for cardiovascular abnormalities.

Data Augmentation for Skin Lesion Analysis

The results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images.

The Effectiveness of Data Augmentation in Image Classification using Deep Learning

A method to allow a neural net to learn augmentations that best improve the classifier, which is called neural augmentation is proposed, and the successes and shortcomings of this method are discussed.

Biomedical Data Augmentation Using Generative Adversarial Neural Networks

This work proposes a new application of GANNs to the automatic generation of artificial Magnetic Resonance Images (MRI) of slices of the human brain; both quantitative and human-based evaluations of generated images have been carried out in order to assess effectiveness of the method.

Learning from Simulated and Unsupervised Images through Adversarial Training

This work develops a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors, and makes several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training.

DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification

A new discriminator loss is proposed to fit the goal of data augmentation, through which both real and augmented samples are enforced to contribute to and be consistent in finding the decision boundaries.
...