scholarly journals Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation

2020 ◽  
pp. 41-58 ◽  
Author(s):  
Zongsheng Yue ◽  
Qian Zhao ◽  
Lei Zhang ◽  
Deyu Meng
Keyword(s):  
Real World ◽  
Noise Removal ◽  
Noise Generation ◽  
Adversarial Network

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

scholarly journals Using the Generative Adversarial Network to Generate Recommendations

2020 ◽  
Author(s):  
A.V. Prosvetov
Keyword(s):  
Real World ◽  
Recommendation System ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Theoretical Comparison ◽  
New Methods ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Recommendation Algorithms ◽  
Cold Start Problem

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.

scholarly journals A Method of Offline Reinforcement Learning Virtual Reality Satellite Attitude Control Based on Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jian Zhang ◽  
Fengge Wu
Keyword(s):  
Virtual Reality ◽  
Reinforcement Learning ◽  
Real World ◽  
Attitude Control ◽  
Control Method ◽  
Sensor Data ◽  
Generative Adversarial Network ◽  
Goal State ◽  
Safety Issues ◽  
Adversarial Network

Virtual reality satellites give people an immersive experience of exploring space. The intelligent attitude control method using reinforcement learning to achieve multiaxis synchronous control is one of the important tasks of virtual reality satellites. In real-world systems, methods based on reinforcement learning face safety issues during exploration, unknown actuator delays, and noise in the raw sensor data. To improve the sample efficiency and avoid safety issues during exploration, this paper proposes a new offline reinforcement learning method to make full use of samples. This method learns a policy set with imitation learning and a policy selector using a generative adversarial network (GAN). The performance of the proposed method was verified in a real-world system (reaction-wheel-based inverted pendulum). The results showed that the agent trained with our method reached and maintained a stable goal state in 10,000 steps, whereas the behavior cloning method only remained stable for 500 steps.

scholarly journals Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

2020 ◽  
Vol 34 (01) ◽  
pp. 841-848
Author(s):  
Farzan Masrour ◽  
Tyler Wilson ◽  
Heng Yan ◽  
Pang-Ning Tan ◽  
Abdol Esfahanian
Keyword(s):  
Social Networking ◽  
Real World ◽  
Link Prediction ◽  
Representation Learning ◽  
Experimental Results ◽  
Adversarial Network ◽  
Network Link ◽  
Network Modularity ◽  
Filter Bubble ◽  
Real World Datasets

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.

3D Noise Generation and Removal of Measured Grinding Wheel Topography Using a White Light Interferometer

2011 ◽  
Vol 487 ◽  
pp. 386-390 ◽  
Author(s):  
J. Xie ◽  
F. Zhang ◽  
H.F. Xie
Keyword(s):  
White Light ◽  
Rake Angle ◽  
Noise Removal ◽  
Grinding Wheel ◽  
Noise Generation ◽  
Wheel Surface ◽  
White Light Interferometer ◽  
Wheel Topography ◽  
Grinding Wheel Topography ◽  
Noise Data

In order to precisely measure grain protrusion topography of diamond grinding wheel, the measured noises should be removal, but it has not been clear how these noises are produced. Hence, a white light interferometer was employed to measure the SD60 grinding wheel surface. The objective is to evaluate the grain protrusion parameters. It is shown that the noise generation is related to the orientation of measured point. The noise occurs as its vector angle is less than 45°. Moreover, the grain protrusion topography may be displayed though noise removal, noise-data restructure and grain contour protection. The grain protrusion height, bond-tail height, rake angle and clearance angle are averagely 108.2 μm, 49.1 μm, -68.6º and 26.4º, respectively.

scholarly journals Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2131
Author(s):  
Liang Lu ◽  
Hongbao Zhu ◽  
Junyu Dong ◽  
Yakun Ju ◽  
Huiyu Zhou
Keyword(s):  
3D Reconstruction ◽  
Real World ◽  
Three Dimensional ◽  
Photometric Stereo ◽  
Laser Line ◽  
Spectral Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Dataset ◽  
Dimensional Reconstruction

This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.

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