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 Speech Emotion Recognition using Data Augmentation Method by Cycle-Generative Adversarial Networks

2021 ◽  
Author(s):  
Arash Shilandari ◽  
Hossein Marvi ◽  
Hossein Khosravi
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Speech Processing ◽  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

scholarly journals Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 164 ◽  
Author(s):  
Aggeliki Vlachostergiou ◽  
George Caridakis ◽  
Phivos Mylonas ◽  
Andreas Stafylopatis
Keyword(s):  
Natural Language ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Learning Tasks ◽  
Potential Applications

The ability to learn robust, resizable feature representations from unlabeled data has potential applications in a wide variety of machine learning tasks. One way to create such representations is to train deep generative models that can learn to capture the complex distribution of real-world data. Generative adversarial network (GAN) approaches have shown impressive results in producing generative models of images, but relatively little work has been done on evaluating the performance of these methods for the learning representation of natural language, both in supervised and unsupervised settings at the document, sentence, and aspect level. Extensive research validation experiments were performed by leveraging the 20 Newsgroups corpus, the Movie Review (MR) Dataset, and the Finegrained Sentiment Dataset (FSD). Our experimental analysis suggests that GANs can successfully learn representations of natural language texts at all three aforementioned levels.

scholarly journals Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning

2020 ◽  
Vol 10 (13) ◽  
pp. 4528
Author(s):  
Je-Yeol Lee ◽  
Sang-Il Choi 
Keyword(s):  
Generative Models ◽  
Learning Processes ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Secondary Network ◽  
Variational Autoencoder ◽  
Variational Learning

In this paper, we propose a new network model using variational learning to improve the learning stability of generative adversarial networks (GAN). The proposed method can be easily applied to improve the learning stability of GAN-based models that were developed for various purposes, given that the variational autoencoder (VAE) is used as a secondary network while the basic GAN structure is maintained. When the gradient of the generator vanishes in the learning process of GAN, the proposed method receives gradient information from the decoder of the VAE that maintains gradient stably, so that the learning processes of the generator and discriminator are not halted. The experimental results of the MNIST and the CelebA datasets verify that the proposed method improves the learning stability of the networks by overcoming the vanishing gradient problem of the generator, and maintains the excellent data quality of the conventional GAN-based generative models.

scholarly journals Human Face Generation using Deep Convolution Generative Adversarial Network

2021 ◽  
Vol 7 (01) ◽  
pp. 114-120
Author(s):  
Chaudhary Sarimurrab, Ankita Kesari Naman and Sudha Narang
Keyword(s):  
Random Noise ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Practical Applications ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Face Generation ◽  
Human Faces

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.

Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks

2018 ◽  
Author(s):  
Wei Chen ◽  
Ashwin Jeyaseelan ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Design Exploration ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Latent Space ◽  
Latent Representations

Real-world designs usually consist of parts with hierarchical dependencies, i.e., the geometry of one component (a child shape) is dependent on another (a parent shape). We propose a method for synthesizing this type of design. It decomposes the problem of synthesizing the whole design into synthesizing each component separately but keeping the inter-component dependencies satisfied. This method constructs a two-level generative adversarial network to train two generative models for parent and child shapes, respectively. We then use the trained generative models to synthesize or explore parent and child shapes separately via a parent latent representation and infinite child latent representations, each conditioned on a parent shape. We evaluate and discuss the disentanglement and consistency of latent representations obtained by this method. We show that shapes change consistently along any direction in the latent space. This property is desirable for design exploration over the latent space.

scholarly journals Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazuma Kokomoto ◽  
Rena Okawa ◽  
Kazuhiko Nakano ◽  
Kazunori Nozaki
Keyword(s):  
Private Information ◽  
Data Augmentation ◽  
Wasserstein Distance ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Pediatric Dentists ◽  
Clinical Cases

AbstractDentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.

scholarly journals Recent Advancement in Generative Adversarial Network Models

2021 ◽  
pp. 28-32
Author(s):  
Rounit Agrawal ◽  
Sakshi Seth ◽  
Niti Patil
Keyword(s):  
Network Models ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Deep Model ◽  
Conventional Machine ◽  
Zero Sum ◽  
Work Done

GANs (Generative Adversarial Networks) have recently gained a lot of attention in the research community. GANs are based on the zero-sum game theory, in which two neural networks compete for the resources. The results of deep model is capable of producing data that is close to any given data distribution. It employs an adversarial learning method and is much more efficient than conventional machine learning models as learning features. In this paper, firstly discusses the introductory detail about GAN followed by the brief literature survey of work done with GAN models and then followed by its different approaches and discusses how they differ. The analysis then goes on to list of the various applications such as computer vision, image classification and processing of language etc. before coming to a conclusion. As well as, compare this GAN model with other generative models and also mentioned the limitation of GAN.

scholarly journals Generating Scenery Images with Larger Variety According to User Descriptions

2021 ◽  
Vol 11 (21) ◽  
pp. 10224
Author(s):  
Hsu-Yung Cheng ◽  
Chih-Chang Yu
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Memory Cell ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Hidden Layer

In this paper, a framework based on generative adversarial networks is proposed to perform nature-scenery generation according to descriptions from the users. The desired place, time and seasons of the generated scenes can be specified with the help of text-to-image generation techniques. The framework improves and modifies the architecture of a generative adversarial network with attention models by adding the imagination models. The proposed attentional and imaginative generative network uses the hidden layer information to initialize the memory cell of the recurrent neural network to produce the desired photos. A data set containing different categories of scenery images is established to train the proposed system. The experiments validate that the proposed method is able to increase the quality and diversity of the generated images compared to the existing method. A possible application of road image generation for data augmentation is also demonstrated in the experimental results.

GANDaLF: GAN for Data-Limited Fingerprinting

2021 ◽  
Vol 2021 (2) ◽  
pp. 305-322
Author(s):  
Se Eun Oh ◽  
Nate Mathews ◽  
Mohammad Saidur Rahman ◽  
Matthew Wright ◽  
Nicholas Hopper
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Large Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Closed World ◽  
Training Samples ◽  
A Site

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

Data augment method for machine fault diagnosis using conditional generative adversarial networks

2020 ◽  
Vol 234 (12) ◽  
pp. 2719-2727 ◽  
Author(s):  
Jinrui Wang ◽  
Baokun Han ◽  
Huaiqian Bao ◽  
Mingyan Wang ◽  
Zhenyun Chu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Augmentation ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Stacked Autoencoders ◽  
Model Training ◽  
Conditional Information

As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

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