scholarly journals Mixed-Type Data Generation Method Based On Generative Adversarial Networks

2021 ◽  
Author(s):  
Ning Wei ◽  
Longzhi Wang ◽  
Guanhua Chen ◽  
Yirong Wu ◽  
Shuifa Sun ◽  
...  
Keyword(s):  
Policy Research ◽  
Real Data ◽  
Training Data ◽  
Mixed Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Data Generation ◽  
Continuous Space ◽  
The Real ◽  
Adversarial Networks

Abstract Data-driven based deep learing has become a key research direction in the field of artificial intelligence. Abundant training data is a guarantee for building efficient and accurate models. However, due to the privacy protection policy, research institutions are often limited to obtain a large number of training data, which would lead to a lack of training sets circumstance. In this paper, a mixed data generation model (mixGAN) based on generative adversarial networks (GANs) is proposed to synthesize fake data that have the same distribution with the real data, so as to supplement the real data and increase the number of available samples. The model first pre-trains the autoencoder which maps given dataset into a low-dimensional continuous space. Then, the Generator constructed in the low-dimension space is obtained by training it adversarially with Discriminator constructed in the original space. Since the constructed Discriminator not only consider the loss of the continuous attributes but also the labeled attributes, the generator nets formed by the Generator and the decoder can effectively learn the intrinsic distribution of the mixed data. We evaluate the proposed method both in the independent distribution of the attribute and in the relationship of the attributes, and the experiment results show that the proposed generate method has a better performance in preserve the intrinsic distribution compared with other generation algorithms based on deep learning.

scholarly journals Collaborative Sampling in Generative Adversarial Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4948-4956
Author(s):  
Yuejiang Liu ◽  
Parth Kothari ◽  
Alexandre Alahi
Keyword(s):  
Sampling Method ◽  
Data Distribution ◽  
Real Data ◽  
Sampling Scheme ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Standard Practice ◽  
The Real ◽  
Adversarial Networks ◽  
Gradient Based

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a collaborative sampling scheme between the generator and the discriminator for improved data generation. Guided by the discriminator, our approach refines the generated samples through gradient-based updates at a particular layer of the generator, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can smoothen the loss landscape provided by the discriminator for effective sample refinement. Through extensive experiments on synthetic and image datasets, we demonstrate that our proposed method can improve generated samples both quantitatively and qualitatively, offering a new degree of freedom in GAN sampling.

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

scholarly journals Dynamic scene deblurring and image de-raining based on generative adversarial networks and transfer learning for Internet of vehicle

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bingcai Wei ◽  
Liye Zhang ◽  
Kangtao Wang ◽  
Qun Kong ◽  
Zhuang Wang
Keyword(s):  
Transfer Learning ◽  
High Speed ◽  
Image Deblurring ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Dynamic Scene ◽  
The Real ◽  
Adversarial Networks ◽  
Rain Removal ◽  
Residual Block

AbstractExtracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.

scholarly journals Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260308
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Tatiane Espindola ◽  
Aleš Popovič ◽  
Andrea De Lorenzo
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Signal Quality ◽  
Quality Service ◽  
High Quality ◽  
The Real ◽  
Adversarial Networks ◽  
High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

scholarly journals GENERATING SYNTHETIC TRAINING DATA FOR OBJECT DETECTION USING MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 443-449
Author(s):  
Y. Lin ◽  
K. Suzuki ◽  
H. Takeda ◽  
K. Nakamura
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rapid Development ◽  
Real Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Traffic Signs ◽  
Adversarial Networks ◽  
The Neural Network

Abstract. Nowadays, digitizing roadside objects, for instance traffic signs, is a necessary step for generating High Definition Maps (HD Map) which remains as an open challenge. Rapid development of deep learning technology using Convolutional Neural Networks (CNN) has achieved great success in computer vision field in recent years. However, performance of most deep learning algorithms highly depends on the quality of training data. Collecting the desired training dataset is a difficult task, especially for roadside objects due to their imbalanced numbers along roadside. Although, training the neural network using synthetic data have been proposed. The distribution gap between synthetic and real data still exists and could aggravate the performance. We propose to transfer the style between synthetic and real data using Multi-Task Generative Adversarial Networks (SYN-MTGAN) before training the neural network which conducts the detection of roadside objects. Experiments focusing on traffic signs show that our proposed method can reach mAP of 0.77 and is able to improve detection performance for objects whose training samples are difficult to collect.

scholarly journals Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 734 ◽  
Author(s):  
Yan Ma ◽  
Kang Liu ◽  
Zhibin Guan ◽  
Xinkai Xu ◽  
Xu Qian ◽  
...  
Keyword(s):  
Object Recognition ◽  
Synthesis Method ◽  
Interactive Systems ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Augmented Reality (AR) is crucial for immersive Human–Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry . Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

scholarly journals Traffic Accident Data Generation Based on Improved Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5767
Author(s):  
Zhijun Chen ◽  
Jingming Zhang ◽  
Yishi Zhang ◽  
Zihao Huang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Original Data ◽  
Urban Traffic ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Data Generation ◽  
Road Traffic Safety ◽  
Adversarial Networks ◽  
Accident Data

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.

scholarly journals Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

2021 ◽  
Vol 13 (9) ◽  
pp. 1713
Author(s):  
Songwei Gu ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Mengyao Li ◽  
Huamei Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Life ◽  
Attention Mechanism ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Remote Sensing Images ◽  
Training Time ◽  
Adversarial Networks ◽  
Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

scholarly journals A Multi-Resolution Approach to GAN-Based Speech Enhancement

2021 ◽  
Vol 11 (2) ◽  
pp. 721
Author(s):  
Hyung Yong Kim ◽  
Ji Won Yoon ◽  
Sung Jun Cheon ◽  
Woo Hyun Kang ◽  
Nam Soo Kim
Keyword(s):  
Speech Enhancement ◽  
Optimal Solution ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
The Real ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Speech Characteristics ◽  
Conventional Methods ◽  
Convex Property

Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.

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