scholarly journals Seismic Data Augmentation Based on Conditional Generative Adversarial Networks

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6850
Author(s):  
Yuanming Li ◽  
Bonhwa Ku ◽  
Shou Zhang ◽  
Jae-Kwang Ahn ◽  
Hanseok Ko
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Seismic Waveforms ◽  
Adversarial Networks ◽  
Seismic Waveform ◽  
Proposed Model

Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.

scholarly journals Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

2019 ◽  
Vol 9 (18) ◽  
pp. 3908 ◽  
Author(s):  
Jintae Kim ◽  
Shinhyeok Oh ◽  
Oh-Woog Kwon ◽  
Harksoo Kim
Keyword(s):  
Performance Measures ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Training Method ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Previous State ◽  
Vector Representations

To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.

scholarly journals 3dinfogan: 3d Models’ Reconstruction In Infogans

2021 ◽  
Vol 10 (02) ◽  
pp. 95-109
Author(s):  
Du Chunqi ◽  
Shinobu Hasegawa
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Latent Variables ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Real Data ◽  
3D Models ◽  
High Quality ◽  
Shape Constraint ◽  
Real Objects

In computer vision and computer graphics, 3D reconstruction is the process of capturing real objects’ shapes and appearances. 3D models always can be constructed by active methods which use high-quality scanner equipment, or passive methods that learn from the dataset. However, both of these two methods only aimed to construct the 3D models, without showing what element affects the generation of 3D models. Therefore, the goal of this research is to apply deep learning to automatically generating 3D models, and finding the latent variables which affect the reconstructing process. The existing research GANs can be trained in little data with two networks called Generator and Discriminator, respectively. Generator can produce synthetic data, and Discriminator can discriminate between the generator’s output and real data. The existing research shows that InFoGAN can maximize the mutual information between latent variables and observation. In our approach, we will generate the 3D models based on InFoGAN and design two constraints, shape-constraint and parameters-constraint, respectively. Shape-constraint utilizes the data augmentation method to limit the synthetic data generated in the models’ profiles. At the same time, we also try to employ parameters-constraint to find the 3D models’ relationship corresponding to the latent variables. Furthermore, our approach will be a challenge in the architecture of generating 3D models built on InFoGAN. Finally, in the process of generation, we might discover the contribution of the latent variables influencing the 3D models to the whole network.

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 Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Richa Sharma ◽  
Manoj Sharma ◽  
Ankit Shukla ◽  
Santanu Chaudhury
Keyword(s):  
Object Tracking ◽  
Action Recognition ◽  
Synthetic Data ◽  
Depth Map ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Spatio Temporal ◽  
Class Labels

Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.

scholarly journals Improving protein function prediction with synthetic feature samples created by generative adversarial networks

2019 ◽  
Author(s):  
Cen Wan ◽  
David T. Jones
Keyword(s):  
Protein Function ◽  
Data Augmentation ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Generative Adversarial Networks ◽  
Prediction Methods ◽  
High Quality ◽  
Adversarial Networks ◽  
Synthetic Protein ◽  
Protein Feature

AbstractProtein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of the Gene Ontology through augmentation of the original training protein feature samples.

scholarly journals Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bo Pan ◽  
Wei Zheng
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Band ◽  
Computational Models ◽  
Data Augmentation ◽  
Learning Model ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Before And After ◽  
Deep Learning Model

Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.

scholarly journals Effective Data Augmentation with Multi-Domain Learning GANs

2020 ◽  
Vol 34 (04) ◽  
pp. 6566-6574 ◽  
Author(s):  
Shin'ya Yamaguchi ◽  
Sekitoshi Kanai ◽  
Takeharu Eda
Keyword(s):  
Data Augmentation ◽  
Scene Recognition ◽  
Generative Adversarial Networks ◽  
Massive Data ◽  
Practical Applications ◽  
Domain Fusion ◽  
Adversarial Networks ◽  
Domain Learning ◽  
Classification Tasks

For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously high costs. In this work, we propose an effective data augmentation method based on generative adversarial networks (GANs), called Domain Fusion. Our key idea is to import the knowledge contained in an outer dataset to a target model by using a multi-domain learning GAN. The multi-domain learning GAN simultaneously learns the outer and target dataset and generates new samples for the target tasks. The simultaneous learning process makes GANs generate the target samples with high fidelity and variety. As a result, we can obtain accurate models for the target tasks by using these generated samples even if we only have an extremely low volume target dataset. We experimentally evaluate the advantages of Domain Fusion in image classification tasks on 3 target datasets: CIFAR-100, FGVC-Aircraft, and Indoor Scene Recognition. When trained on each target dataset reduced the samples to 5,000 images, Domain Fusion achieves better classification accuracy than the data augmentation using fine-tuned GANs. Furthermore, we show that Domain Fusion improves the quality of generated samples, and the improvements can contribute to higher accuracy.

scholarly journals GGADN: Guided Generative Adversarial Dehazing Network

2021 ◽  
Author(s):  
Zhang Jian ◽  
Wanjuan Song
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Minimax Problem ◽  
Feature Model ◽  
Generative Adversarial Networks ◽  
Image Dehazing ◽  
Learning Methods ◽  
Adversarial Networks ◽  
Network Training

Abstract Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

scholarly journals Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Sajila D. Wickramaratne ◽  
Md.Shaad Mahmud
Keyword(s):  
Deep Learning ◽  
Near Infrared ◽  
Data Augmentation ◽  
Learning Task ◽  
Generative Adversarial Networks ◽  
Functional Near Infrared Spectroscopy ◽  
Learning Classifier ◽  
Adversarial Networks ◽  
Task Classification ◽  
Specific Category

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject’s task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

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