scholarly journals Modeling and Analysis of Particle Deposition Processes on PVDF Membranes Using SEM Images and Image Generation by Auxiliary Classifier Generative Adversarial Networks

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2225
Author(s):  
Caterina Cacciatori ◽  
Takashi Hashimoto ◽  
Satoshi Takizawa
Keyword(s):  
Membrane Fouling ◽  
Particle Deposition ◽  
Spatial Clustering ◽  
Complex Structure ◽  
Generative Adversarial Networks ◽  
Membrane Structures ◽  
Sem Images ◽  
The Real ◽  
Adversarial Networks ◽  
Particle Counting

Due to highly complex membrane structures, previous research on membrane modeling employed extensively simplified structures to save computational expense, which resulted in deviation from the real processes of membrane fouling. To overcome those shortcomings of the previous models, this study aimed to provide an alternative method of modeling membrane fouling in water filtration, using auxiliary classifier generative adversarial networks (ACGAN). Scanning electron microscope (SEM) images of 0.45 µm polyvinylidene difluoride (PVDF) flat sheet membranes were taken as inputs to ACGAN, before and after the filtration of feed waters containing 0.5 µm diameter particles at varied concentrations. The images generated with the ACGAN model successfully reconstructed the real images of particles deposited on the membranes, as verified by human validation and particle counting of the real and generated images. This indicated that the ACGAN model developed in this research successfully built a model architecture that represents the complex structure of the real PVDF membrane. The image analysis through particle counting and density-based spatial clustering of application with noise (DBSCAN) revealed that both real and generated membranes had an uneven deposition of particles, which was caused by the complex structures of the membranes and by different particle concentrations. These results indicated the importance and effectiveness of modeling intact membranes, without simplifying the structure using such models as the ACGAN model presented in this paper.

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.

scholarly journals EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4852-4859
Author(s):  
Jinduo Liu ◽  
Junzhong Ji ◽  
Guangxu Xun ◽  
Liuyi Yao ◽  
Mengdi Huai ◽  
...  
Keyword(s):  
Time Series ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Effective Connectivity ◽  
Small Sample ◽  
Fmri Data ◽  
Generative Adversarial Networks ◽  
The Real ◽  
Adversarial Networks ◽  
Fmri Time Series

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data.

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 Synthesis of glioma histopathology images using generative adversarial networks

2021 ◽  
Vol 48 (s1) ◽  
pp. S3-S3
Author(s):  
AB Levine ◽  
J Peng ◽  
SJM Jones ◽  
A Bashashati ◽  
S Yip
Keyword(s):  
Deep Learning ◽  
Image Synthesis ◽  
The Cancer Genome Atlas ◽  
Generative Adversarial Networks ◽  
List Type ◽  
The Real ◽  
Training Images ◽  
Adversarial Networks ◽  
Histopathology Images ◽  
Generative Modelling

Deep learning, a subset of artificial intelligence, has shown great potential in several recent applications to pathology. These have mainly involved the use of classifiers to diagnose disease, while generative modelling techniques have been less frequently used. Generative adversarial networks (GANs) are a type of deep learning model that has been used to synthesize realistic images in a range of domains, both general purpose and medical. In the GAN framework, a generator network is trained to synthesize fake images, while a dueling discriminator network aims to distinguish between the fake images and a set of real training images. As GAN training progresses, the generator network ideally learns the important features of a dataset, allowing it to create images that the discriminator cannot distinguish from the real ones. We report on our use of GANs to synthesize high resolution, realistic histopathology images of gliomas. The well- known Progressive GAN framework was trained on a set of image patches extracted from digital slides in the Cancer Genome Atlas repository, and was able to generate fake images that were visually indistinguishable from the real training images. Generative modelling in pathology has numerous potential applications, including dataset augmentation for training deep learning classifiers, image processing, and expanding educational material.LEARNING OBJECTIVESThis presentation will enable the learner to: 1.Explain basic principles of generative modelling in deep learning.2.Discuss applications of deep learning to neuropathology image synthesis.

scholarly journals The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3145 ◽  
Author(s):  
Yuantao Chen ◽  
Jiajun Tao ◽  
Jin Wang ◽  
Xi Chen ◽  
Jingbo Xie ◽  
...  
Keyword(s):  
Sensor Network ◽  
Loss Function ◽  
Network Structure ◽  
Posterior Probability ◽  
Feature Matching ◽  
Generative Adversarial Networks ◽  
Classification Algorithms ◽  
The Novel ◽  
The Real ◽  
Adversarial Networks

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.

scholarly journals Dynamic Scene Deblurring and Image De-Raining Based on Generative Adversarial Networks and Transfer Learning for Internet of Vehicle

2021 ◽  
Author(s):  
Bingcai Wei ◽  
liye zhang ◽  
Kangtao Wang ◽  
Qun Kong ◽  
Zhuang Wang
Keyword(s):  
Transfer Learning ◽  
High Speed ◽  
Image Deblurring ◽  
Motion Blur ◽  
Generative Adversarial Networks ◽  
Dynamic Scene ◽  
Generative Adversarial Network ◽  
The Real ◽  
Adversarial Networks ◽  
Residual Block

Abstract Extracting traffic information from images plays an important role in Internet of Vehicle (IoV). However, due to the high-speed movement and bumpiness of the vehicle, motion blur will occur in image acquisition. In addition, in rainy days, because the rain is 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 cause traffic accidents. In this paper, we propose a motion blurred restoration and rain removal algorithm for IoV based on Generative Adversarial Network (GAN) and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical tasks 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 Leakly-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblock, 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, we can use the pre-trained model for the transfer learning-based image de-raining task and show good results on several datasets.

scholarly journals Improving GAN with Neighbors Embedding and Gradient Matching

2019 ◽  
Vol 33 ◽  
pp. 5191-5198
Author(s):  
Ngoc-Trung Tran ◽  
Tuan-Anh Bui ◽  
Ngai-Man Cheung
Keyword(s):  
Generative Adversarial Networks ◽  
New Techniques ◽  
Real Samples ◽  
The Real ◽  
Local Structures ◽  
Adversarial Networks ◽  
The Difference ◽  
A New Technique ◽  
Discriminator Function

We propose two new techniques for training Generative Adversarial Networks (GANs) in the unsupervised setting. Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan

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.

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