scholarly journals Realistic Face Image Generation System Based on GANs

2020 ◽  
Author(s):  
Zhike Han ◽  
Bin Yang ◽  
Yiren Du ◽  
Xingyu Du ◽  
Hao Xing ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Generation System ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Face Generation

The purpose of this paper is to study the help of generative adversarial networks (GAN) for face generation, and to explore whether the network can have an effect on complex face generation. Training an image translation neural network model based on a generative adversarial network with the help of a large number of real human face data sets. Using the CV2-based face tagging algorithm and the HED-based face edge extraction algorithm to obtain input information, and then based on the translation neural network model Developing a face generation system through Tensorflow, Torch and other frameworks to realize the function of generating real faces through sketches or “changing faces” through existing faces. Finally, this model provides training configuration and training information.

A Generative Adversarial Network Model for Simulating Various Types of Human-Induced Loads

2019 ◽  
Vol 19 (08) ◽  
pp. 1950092 ◽  
Author(s):  
Jiecheng Xiong ◽  
Jun Chen
Keyword(s):  
Network Model ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Real Samples ◽  
Load Model ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Power Spectral ◽  
Physical Features ◽  
Fully Connected

Severe vibrations may occur on slender structures like footbridges and cantilever stands due to human-induced loads such as walking, jumping or bouncing. Currently, to develop a load model for structural design, the main features, such as periodicity and stationarity of experimental load records, are artificially extracted and then mathematically modeled. Different physical features have been included in different load models, i.e. no unified load model exists for different individual activities. The recently emerged generative adversarial networks can be used to model high-dimensional random variables. The probability distribution of these variables learned from real samples can be used to generate new samples, avoiding extracting features artificially. In this paper, a new model is proposed which combines the conditional generative adversarial networks and Wasserstein generative adversarial networks with gradient penalty to generate individual walking, jumping and bouncing loads. The generator of the model has five fully connected layers and a one-dimensional convolutional layer, and the discriminator has five fully connected layers. After one million training steps using the experimental records, the generator can generate high-quality samples similar to real samples in waveform. Finally, by comparing the power spectral densities and single degree of freedom system’s responses of the generated samples with real samples, it is further proved that the proposed generative adversarial network model can be used to simulate various human-induced loads. Source code of the model along with its trained weights is provided to the readers to further analysis and application.

scholarly journals A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3269 ◽  
Author(s):  
Hongmin Gao ◽  
Dan Yao ◽  
Mingxia Wang ◽  
Chenming Li ◽  
Haiyun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
The One ◽  
Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

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 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.

CAMGAN: Combining attention mechanism generative adversarial networks for cartoon face style transfer

2021 ◽  
pp. 1-9
Author(s):  
Tao Zhang ◽  
Long Yu ◽  
Shengwei Tian
Keyword(s):  
Real World ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Model Parameters ◽  
Data Sets ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Image Translation

In this paper, we presents an apporch for real-world human face close-up images cartoonization. We use generative adversarial network combined with an attention mechanism to convert real-world face pictures and cartoon-style images as unpaired data sets. At present, the image-to-image translation model has been able to successfully transfer style and content. However, some problems still exist in the task of cartoonizing human faces:Hunman face has many details, and the content of the image is easy to lose details after the image is translated. the quality of the image generated by the model is defective. The model in this paper uses the generative adversarial network combined with the attention mechanism, and proposes a new generative adversarial network combined with the attention mechanism to deal with these problems. The channel attention mechanism is embedded between the upper and lower sampling layers of the generator network, to avoid increasing the complexity of the model while conveying the complete details of the underlying information. After comparing the experimental results of FID, PSNR, MSE three indicators and the size of the model parameters, the new model network proposed in this paper avoids the complexity of the model while achieving a good balance in the conversion task of style and content.

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.

RESEARCH OF GENERATIVE ADVERSARIAL NETWORKS FOR THE SYNTHESIS OF NEW MEDICAL DATA

2020 ◽  
Vol 2020 (2) ◽  
pp. 17-23
Author(s):  
Vladislav Laptev ◽  
Vyacheslav Danilov ◽  
Olga Gerget
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Medical Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Training Procedure ◽  
Neural Network Architecture ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

The paper considers the development of a Generative Adversarial Network (GAN) for the synthesis of new medical data. The developed GAN consists of two models trained simultaneously: a generative model (G - Generator), estimating the distribution of data, and a discriminating model (D - Discriminator), which estimates the probability that the sample is obtained from the training data, and not from generator G. To create G, we used own neural network architecture based on convolutional layers using experimental functions of Tensor Flow Addons. To create discriminator D, we used a Transfer Learning (TL) approach. The training procedure is to maximize the likelihood that discriminator D will make a mistake. Experiments show that the proposed GAN architecture completely copes with the task of synthesizing of new medical data.

scholarly journals Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Pengfei Zhang ◽  
Xiaoming Ju
Keyword(s):  
Neural Network ◽  
Gaussian Mixture ◽  
Physical World ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Testing Stage

It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

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