Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.

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CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

Applied Sciences ◽

10.3390/app11041380 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1380

Author(s):

Yingbo Zhou ◽

Pengcheng Zhao ◽

Weiqin Tong ◽

Yongxin Zhu

Keyword(s):

Distance Learning ◽

Feature Learning ◽

Image Synthesis ◽

Image Feature ◽

Generative Adversarial Networks ◽

Image Generation ◽

Generative Adversarial Network ◽

Feature Representations ◽

Adversarial Network ◽

Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

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FLGC-Fusion GAN: An Enhanced Fusion GAN Model by Importing Fully Learnable Group Convolution

Mathematical Problems in Engineering ◽

10.1155/2020/6384831 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

C. Yuan ◽

C. Q. Sun ◽

X. Y. Tang ◽

R. F. Liu

Keyword(s):

Generative Adversarial Networks ◽

Visual Effects ◽

Generative Adversarial Network ◽

Composite Image ◽

Adversarial Network ◽

Adversarial Networks ◽

Network Building ◽

Fusion Methods ◽

Basic Network ◽

Public Datasets

The purpose of image fusion is to combine the source images of the same scene into a single composite image with more useful information and better visual effects. Fusion GAN has made a breakthrough in this field by proposing to use the generative adversarial network to fuse images. In some cases, considering retain infrared radiation information and gradient information at the same time, the existing fusion methods ignore the image contrast and other elements. To this end, we propose a new end-to-end network structure based on generative adversarial networks (GANs), termed as FLGC-Fusion GAN. In the generator, using the learnable grouping convolution can improve the efficiency of the model and save computing resources. Therefore, we can have a better trade-off between the accuracy and speed of the model. Besides, we take the residual dense block as the basic network building unit and use the perception characteristics of the inactive as content loss characteristics of input, achieving the effect of deep network supervision. Experimental results on two public datasets show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.

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Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification

Complexity ◽

10.1155/2020/8516216 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Tianshi Wang ◽

Li Liu ◽

Huaxiang Zhang ◽

Long Zhang ◽

Xiuxiu Chen

Keyword(s):

Text Classification ◽

Real Data ◽

Training Sample ◽

Generative Adversarial Networks ◽

Adversarial Network ◽

Classification Framework ◽

Training Samples ◽

Text Classifiers ◽

Textual Features ◽

Public Datasets

With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples. In this paper, we propose a text classification framework under insufficient training sample conditions. In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of generated data consistent with that of real data. Finally, the classifier is cooperatively trained by real data and generated data. Extensive experimental validation on four public datasets demonstrates that our method significantly performs better than the comparative methods.

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Road Topology Refinement via a Multi-Conditional Generative Adversarial Network

Sensors ◽

10.3390/s19051162 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1162

Author(s):

Yang Zhang ◽

Xiang Li ◽

Qianyu Zhang

Keyword(s):

Road Network ◽

Rapid Development ◽

Road Networks ◽

Extraction Methods ◽

Road Extraction ◽

Generative Adversarial Network ◽

Topological Information ◽

The Road ◽

Adversarial Network ◽

Spectral Performance

With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies. Based on the topological networks produced by previous road extraction methods, in this paper, we propose a Multi-conditional Generative Adversarial Network (McGAN) to obtain complete road networks by refining the imperfect road topology. The proposed McGAN, which is composed of two discriminators and a generator, takes both original remote sensing image and the initial road network produced by existing road extraction methods as input. The first discriminator employs the original spectral information to instruct the reconstruction, and the other discriminator aims to refine the road network topology. Such a structure makes the generator capable of receiving both spectral and topological information of the road region, thus producing more complete road networks compared with the initial road network. Three different datasets were used to compare McGan with several recent approaches, which showed that the proposed method significantly improved the precision and recall of the road networks, and also worked well for those road regions where previous methods could hardly obtain complete structures.

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Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.723843 ◽

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.

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GANDaLF: GAN for Data-Limited Fingerprinting

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2021-0029 ◽

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.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

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.

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Restoring Raindrops Using Attentive Generative Adversarial Networks

Applied Sciences ◽

10.3390/app11157034 ◽

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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Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

BMC Bioinformatics ◽

10.1186/s12859-021-04101-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yingxi Yang ◽

Hui Wang ◽

Wen Li ◽

Xiaobo Wang ◽

Shizhao Wei ◽

...

Keyword(s):

Correlation Coefficient ◽

Sequence Data ◽

Rapid Development ◽

Pearson Correlation ◽

Structural Features ◽

Generative Adversarial Networks ◽

Post Translational Modification ◽

Generative Adversarial Network ◽

Data Imbalance ◽

Adversarial Network

Abstract Background Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. Method We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. Results In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN. Conclusions The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

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Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Electronics ◽

10.3390/electronics10111349 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1349

Author(s):

Stefan Lattner ◽

Javier Nistal

Keyword(s):

Data Storage ◽

Audio Signal ◽

Human Perception ◽

Generative Adversarial Networks ◽

Audio Signals ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Extensive Evaluation ◽

Listening Tests ◽

Musical Audio

Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs may introduce a variety of impairments in the audio signal. Many works have tackled the problem of audio enhancement and compression artifact removal using deep-learning techniques. However, only a few works tackle the restoration of heavily compressed audio signals in the musical domain. In such a scenario, there is no unique solution for the restoration of the original signal. Therefore, in this study, we test a stochastic generator of a Generative Adversarial Network (GAN) architecture for this task. Such a stochastic generator, conditioned on highly compressed musical audio signals, could one day generate outputs indistinguishable from high-quality releases. Therefore, the present study may yield insights into more efficient musical data storage and transmission. We train stochastic and deterministic generators on MP3-compressed audio signals with 16, 32, and 64 kbit/s. We perform an extensive evaluation of the different experiments utilizing objective metrics and listening tests. We find that the models can improve the quality of the audio signals over the MP3 versions for 16 and 32 kbit/s and that the stochastic generators are capable of generating outputs that are closer to the original signals than those of the deterministic generators.

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