Smart Augmentation Learning an Optimal Data Augmentation Strategy

Lemley, Joseph; Bazrafkan, Shabab; Corcoran, Peter

doi:10.1109/access.2017.2696121

Cited by 350 publications

(218 citation statements)

References 11 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…The 90% percent was divided into 80% for training and 20% for the validation. The selection of 80% for the training and 20% in the validation proved it is efficient in many types of research such as [53][54][55][56][57]. The training data then divided into mini-batches, each of size = 64, such that ( 9 ; 9 ) ∈ (X /0123 ; Y /0123 ); = 1,2, … , > ?…”

Section: The Proposed Modelmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

View full text Add to dashboard Cite

The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

show abstract

Section: The Proposed Modelmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

View full text Add to dashboard Cite

show abstract

“…Data augmentation often includes the application of blurring, rotation, and translation to existing images that allow a network to generalize better. However, not all data augmentation methods can improve performance, and should not be used "blindly" [27]. For example, on MNIST (a Latin handwritten number database), if rotation is used, the network will be unable to distinguish accurately between handwritten "6" and "9" digits [21].…”

Section: Error Criteriamentioning

confidence: 99%

Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network

Mudhsh

Almodfer

2017

Information

View full text Add to dashboard Cite

Abstract:The traditional algorithms for recognizing handwritten alphanumeric characters are dependent on hand-designed features. In recent days, deep learning techniques have brought about new breakthrough technology for pattern recognition applications, especially for handwritten recognition. However, deeper networks are needed to deliver state-of-the-art results in this area. In this paper, inspired by the success of the very deep state-of-the-art VGGNet, we propose Alphanumeric VGG net for Arabic handwritten alphanumeric character recognition. Alphanumeric VGG net is constructed by thirteen convolutional layers, two max-pooling layers, and three fully-connected layers. The proposed model is fast and reliable, which improves the classification performance. Besides, this model has also reduced the overall complexity of VGGNet. We evaluated our approach on two benchmarking databases. We have achieved very promising results, with a validation accuracy of 99.66% for the ADBase database and 97.32% for the HACDB database.

show abstract

“…However, many special training tricks hinder its real application. Neural Augmentation [30] and Smart Augmentation methods [31] teach the neural network autonomous learning how to generate new samples by minimizing the error of that network. e appearance of Generative Adversarial Networks (GANs) provides a new research direction for data augmentation.…”

Section: Related Workmentioning

confidence: 99%

A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Zheng

Tian

Jiang

et al. 2020

Discrete Dynamics in Nature and Society

142

View full text Add to dashboard Cite

Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. Simultaneous data augmentation during training and testing stages can ensure network optimization and enhance its generalization ability. Augmentation in two stages needs to be consistent to ensure the accurate transfer of specific domain information. Furthermore, this framework is universal for any network architecture and data augmentation strategy and therefore can be applied to a variety of deep learning based tasks. Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results.

show abstract

Smart Augmentation Learning an Optimal Data Augmentation Strategy

Cited by 350 publications

References 11 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network

A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Contact Info

Product

Resources

About