scholarly journals Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

2018 ◽  
pp. 594-603 ◽  
Author(s):  
Antreas Antoniou ◽  
Amos Storkey ◽  
Harrison Edwards
Keyword(s):  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Using Data

scholarly journals Indoor Localization using Data Augmentation via Selective Generative Adversarial Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wafa Njima ◽  
Marwa Chafii ◽  
Arsenia Chorti ◽  
Raed M. Shubair ◽  
H. Vincent Poor
Keyword(s):  
Data Augmentation ◽  
Indoor Localization ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Using Data

scholarly journals Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Guangcheng Bao ◽  
Bin Yan ◽  
Li Tong ◽  
Jun Shu ◽  
Linyuan Wang ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Data Augmentation ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Training Samples ◽  
Using Data ◽  
Effective Models

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

scholarly journals Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7975
Author(s):  
Alberto Montero ◽  
Elisenda Bonet-Carne ◽  
Xavier Paolo Burgos-Artizzu
Keyword(s):  
Data Augmentation ◽  
Fetal Brain ◽  
Area Under The Curve ◽  
Generative Adversarial Networks ◽  
Brain Images ◽  
Fine Grained ◽  
Adversarial Networks ◽  
Augmentation Strategies ◽  
Brain Ultrasound ◽  
Using Data

Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.

scholarly journals Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

2021 ◽  
Vol 11 (14) ◽  
pp. 6368
Author(s):  
Fátima A. Saiz ◽  
Garazi Alfaro ◽  
Iñigo Barandiaran ◽  
Manuel Graña
Keyword(s):  
Ad Hoc ◽  
Data Augmentation ◽  
Semantic Network ◽  
Semantic Networks ◽  
Stereo Image ◽  
Generative Adversarial Networks ◽  
Specific Class ◽  
Adversarial Networks ◽  
Augmentation Techniques ◽  
Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

Data Augmentation for Small Face Datasets and Face Verification by Generative Adversarial Networks Inversion

2021 ◽  
Author(s):  
Dinh Tan Nguyen ◽  
Cao Truong Tran ◽  
Trung Thanh Nguyen ◽  
Cao Bao Hoang ◽  
Van Phu Luu ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Face Verification ◽  
Adversarial Networks ◽  
Face Datasets

scholarly journals Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images

2019 ◽  
Vol 8 (9) ◽  
pp. 390 ◽  
Author(s):  
Kun Zheng ◽  
Mengfei Wei ◽  
Guangmin Sun ◽  
Bilal Anas ◽  
Yu Li
Keyword(s):  
Remote Sensing ◽  
Traffic Control ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Vehicle Data ◽  
Augmentation Strategy

Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.

Sign in / Sign up

Export Citation Format

Share Document