scholarly journals Achieving Causal Fairness through Generative Adversarial Networks

2019 ◽  
Author(s):  
Depeng Xu ◽  
Yongkai Wu ◽  
Shuhan Yuan ◽  
Lu Zhang ◽  
Xintao Wu
Keyword(s):  
Causal Model ◽  
Real Data ◽  
Causal Modeling ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Modeling Framework ◽  
High Quality ◽  
Causal Graph ◽  
Adversarial Networks ◽  
Real Distribution

Achieving fairness in learning models is currently an imperative task in machine learning. Meanwhile, recent research showed that fairness should be studied from the causal perspective, and proposed a number of fairness criteria based on Pearl's causal modeling framework. In this paper, we investigate the problem of building causal fairness-aware generative adversarial networks (CFGAN), which can learn a close distribution from a given dataset, while also ensuring various causal fairness criteria based on a given causal graph. CFGAN adopts two generators, whose structures are purposefully designed to reflect the structures of causal graph and interventional graph. Therefore, the two generators can respectively simulate the underlying causal model that generates the real data, as well as the causal model after the intervention. On the other hand, two discriminators are used for producing a close-to-real distribution, as well as for achieving various fairness criteria based on causal quantities simulated by generators. Experiments on a real-world dataset show that CFGAN can generate high quality fair data.

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.

Analysis and application of generative-adeversarial networks for producing high quality images

Ergodesign ◽  
2020 ◽  
Vol 2020 (4) ◽  
pp. 167-176
Author(s):  
Yuriy Malakhov ◽  
Aleksandr Androsov ◽  
Andrey Averchenkov
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
High Quality ◽  
Adversarial Networks ◽  
Network Operation

The article discusses generative adversarial networks for obtaining high quality images. Models, architecture and comparison of network operation are presented. The features of building deep learning models in the process of performing the super-resolution task, as well as methods associated with improving performance, are considered.

Analysis and application of generative-adeversarial networks for producing high quality images

Ergodesign ◽  
2020 ◽  
Vol 2020 (4) ◽  
pp. 165-176
Author(s):  
Yuriy Malakhov ◽  
Aleksandr Androsov ◽  
Andrey Averchenkov
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
High Quality ◽  
Adversarial Networks ◽  
Network Operation

The article discusses generative adversarial networks for obtaining high quality images. Models, architecture and comparison of network operation are presented. The features of building deep learning models in the process of performing the super-resolution task, as well as methods associated with improving performance, are considered.

scholarly journals Dynamics of Fourier Modes in Torus Generative Adversarial Networks

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 325
Author(s):  
Ángel González-Prieto ◽  
Alberto Mozo ◽  
Edgar Talavera ◽  
Sandra Gómez-Canaval
Keyword(s):  
Fourier Series ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Training Process ◽  
Small Perturbations ◽  
Adversarial Networks ◽  
Novel Method ◽  
Truncated Fourier Series ◽  
Real Flow ◽  
Machine Learning Models

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs.

A Model-Invariant Theory of Causation

2021 ◽  
Vol 130 (1) ◽  
pp. 45-96
Author(s):  
J. Dmitri Gallow
Keyword(s):  
Invariant Theory ◽  
Causal Model ◽  
Causal Modeling ◽  
Modeling Framework

This article provides a theory of causation in the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn’t cause) E in a causal model, M, then it will continue to say that that C caused (didn’t cause) E once one has removed an inessential variable from M. The article suggests that, if this theory is true, then one should understand a cause as something which transmits deviant or noninertial behavior to its effect.

Towards High-Performance Deep Learning Models in Tool Wear Classification with Generative Adversarial Networks

2021 ◽  
pp. 117484
Author(s):  
Dirk Alexander Molitor ◽  
Christian Kubik ◽  
Marco Becker ◽  
Ruben Helmut Hetfleisch ◽  
Fan Lyu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
High Performance ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Adversarial Networks ◽  
Tool Wear Classification

High-Quality Sonar Image Generation Algorithm Based on Generative Adversarial Networks

2021 ◽  
Author(s):  
Zhengyang Wang ◽  
Qingchang Guo ◽  
Min Lei ◽  
Shuxiang Guo ◽  
Xiufen Ye
Keyword(s):  
Generative Adversarial Networks ◽  
Image Generation ◽  
High Quality ◽  
Generation Algorithm ◽  
Sonar Image ◽  
Adversarial Networks

scholarly journals RoCGAN: Robust Conditional GAN

2020 ◽  
Vol 128 (10-11) ◽  
pp. 2665-2683 ◽  
Author(s):  
Grigorios G. Chrysos ◽  
Jean Kossaifi ◽  
Stefanos Zafeiriou
Keyword(s):  
Large Scale ◽  
Real Data ◽  
Superior Performance ◽  
Target Space ◽  
Generative Adversarial Networks ◽  
Natural Scenes ◽  
Adversarial Networks ◽  
Target Manifold ◽  
The Face ◽  
Intense Noise

Abstract Conditional image generation lies at the heart of computer vision and conditional generative adversarial networks (cGAN) have recently become the method of choice for this task, owing to their superior performance. The focus so far has largely been on performance improvement, with little effort in making cGANs more robust to noise. However, the regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGANs unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue. Specifically, we augment the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold, even in the presence of intense noise. We prove that RoCGAN share similar theoretical properties as GAN and establish with both synthetic and real data the merits of our model. We perform a thorough experimental validation on large scale datasets for natural scenes and faces and observe that our model outperforms existing cGAN architectures by a large margin. We also empirically demonstrate the performance of our approach in the face of two types of noise (adversarial and Bernoulli).

scholarly journals Smooth Deep Image Generator from Noises

2019 ◽  
Vol 33 ◽  
pp. 3731-3738 ◽  
Author(s):  
Tianyu Guo ◽  
Chang Xu ◽  
Boxin Shi ◽  
Chao Xu ◽  
Dacheng Tao
Keyword(s):  
Linear Interpolation ◽  
Natural Images ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Adversarial Networks ◽  
Deep Image ◽  
Image Generator ◽  
Input Perturbation ◽  
World Image ◽  
Strong Ability

Generative Adversarial Networks (GANs) have demonstrated a strong ability to fit complex distributions since they were presented, especially in the field of generating natural images. Linear interpolation in the noise space produces a continuously changing in the image space, which is an impressive property of GANs. However, there is no special consideration on this property in the objective function of GANs or its derived models. This paper analyzes the perturbation on the input of the generator and its influence on the generated images. A smooth generator is then developed by investigating the tolerable input perturbation. We further integrate this smooth generator with a gradient penalized discriminator, and design smooth GAN that generates stable and high-quality images. Experiments on real-world image datasets demonstrate the necessity of studying smooth generator and the effectiveness of the proposed algorithm.

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