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Generative Adversarial Networks for Spatio-temporal Data: A Survey [article]

Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim
2021 arXiv   pre-print
In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data.  ...  Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.  ...  Conclusions In this survey, we conducted a comprehensive overview of Generative Adversarial Networks (GANs) for spatio-temporal (ST) data in recent years.  ... 
arXiv:2008.08903v3 fatcat:pbhxbfgw65bodksjdmwazwo4dq

Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [article]

Ehsan Adeli, Jize Zhang, Alexandros A. Taflanidis
2021 arXiv   pre-print
The simulation data corresponds to time-series surge predictions over a number of save points within the geographic domain of interest, creating a spatio-temporal imputation problem where the surge points  ...  Very recently, machine learning techniques such as neural network methods have been developed and employed for missing data imputation tasks.  ...  Acknowledgement Authors would like to thank the Army Corp of Engineers, Coastal Hydraulics Laboratory of the Engineering Research and Development Center for providing access to the storm surge data that  ... 
arXiv:2111.02823v2 fatcat:qjvlvcooxngy7lm2nx6hmyxu6i

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
2021 Data Science and Engineering  
In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer.  ...  With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here  ...  [172] leverage GAN (generative adversarial networks) to generate human mobility routes.  ... 
doi:10.1007/s41019-020-00151-z fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM.  ...  In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM.  ...  Deep Learning for Spatio-Temporal Data Mining: A Survey Senzhang Wang, Jiannong Cao, Fellow, IEEE, Philip S. Yu, Fellow, IEEE, ).  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data

Shahela Saif, Samabia Tehseen, Sumaira Kausar
2018 Sensors  
Any advancements in this field are tied to advances in the interrelated fields of object recognition, spatio- temporal video analysis and semantic segmentation.  ...  Activity recognition is a challenging task since it faces many problems such as occlusion, view point variation, background differences and clutter and illumination variations.  ...  Figure 6 . 6 Spatio-temporal interest point detection for a walking person. Reprinted with permission from [62] .  ... 
doi:10.3390/s18113979 fatcat:cbag7fm5gnetffzdl26se5r2ge

Privacy in trajectory micro-data publishing : a survey [article]

Marco Fiore, Panagiota Katsikouli, Elli Zavou, Mathieu Cunche, Françoise Fessant, Dominique Le Hello, Ulrich Matchi Aivodji, Baptiste Olivier, Tony Quertier, Razvan Stanica
2020 arXiv   pre-print
directions for research.  ...  We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks  ...  Acknowledgements The authors would like to thank Emilie Sirvent-Hien and Marc-Olivier Killijian for the constructive discussions.  ... 
arXiv:1903.12211v3 fatcat:kyz7k56e6bcunkzmsdz7xdx5ri

A Survey on Deep Learning for Human Mobility [article]

Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo
2021 arXiv   pre-print
Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation.  ...  Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing.  ...  Data Training Data Discriminator Fig. 4 . Visual representation of a Generative Adversarial Network (GAN). A GAN is composed of a generator , and a discriminator .  ... 
arXiv:2012.02825v2 fatcat:r7navzojwnaojncfsx3sbnfsze

Mobility Data Analysis and Applications: A mid-year 2021 Survey [article]

Abhishek Singh, Alok Mathur, Alka Asthana, Juliet Maina, Jade Nester, Sai Sri Sathya, Santanu Bhattacharya, Vidya Phalke
2021 arXiv   pre-print
We also discuss privacy-preserving solutions to analyze the mobility data in order to expand its reach towards a wider population.  ...  In this work we review recent works analyzing mobility data and its application in understanding the epidemic dynamics for the COVID-19 pandemic and more.  ...  However, the GPS based data collection system requires a smartphone in majority of the cases and also a software capable of collecting the spatio-temporal data.  ... 
arXiv:2109.07901v1 fatcat:3cz7cyw42bagldq4eax4jdcddq

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems [article]

Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim
2021 arXiv   pre-print
Several available surveys capture STDM advances and report a wealth of important progress in this field.  ...  We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM.  ...  Generative Adversarial Networks (GAN) have also utilised for spatiotemporal modelling, simulation and data generation [73] .  ... 
arXiv:2103.17128v1 fatcat:ci5pt5bytndr5inolznjsaizpi

Transformers in Time Series: A Survey [article]

Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun
2022 arXiv   pre-print
To the best of our knowledge, this paper is the first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data.  ...  We hope this survey will ignite further research interests in time series Transformers.  ...  Spatio-Temporal Forecasting In spatio-temporal forecasting, we need to take into account both temporal and spatio-temporal dependency for accurate forecasting.  ... 
arXiv:2202.07125v3 fatcat:q6vmkszqavbgrdtrbpsgaco7u4

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools [article]

Weiwei Jiang, Jiayun Luo
2022 arXiv   pre-print
Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction.  ...  To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.  ...  A novel off-deployment traffic estimation problem is proposed and defined in [111] and a traffic generative adversarial network approach named TrafficGAN is further proposed to solve this problem, which  ... 
arXiv:2103.11824v2 fatcat:nnvx45qxzvcajneonpnok5tfli

Federated Learning on Non-IID Data: A Survey [article]

Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin
2021 arXiv   pre-print
In this survey, we pro-vide a detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning.  ...  Federated learning is an emerging distributed machine learning framework for privacy preservation.  ...  Temporal skew Temporal skew means the skew in distribution in temporal data, including spatio-temporal data and time-series data, also referred to as time-stamped data.  ... 
arXiv:2106.06843v1 fatcat:qsfetsjmxrb6zhhuesgxcjuxj4

A comprehensive survey on recent deep learning-based methods applied to surgical data [article]

Mansoor Ali, Rafael Martinez Garcia Pena, Gilberto Ochoa Ruiz, Sharib Ali
2022 arXiv   pre-print
In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localisation, segmentation, tracking and 3D scene perception.  ...  While several efforts have been made in this direction, a lack of diverse datasets, as well as very dynamic scenes and its variability in each patient entails major hurdle in accomplishing robust systems  ...  Synthetic data generated by blender in order to used to train adversarial network .  ... 
arXiv:2209.01435v1 fatcat:bwctsbdiuvcpvorcjb5ijgbjlu

Deep Gait Recognition: A Survey [article]

Alireza Sepas-Moghaddam, Ali Etemad
2022 arXiv   pre-print
We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.  ...  We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize  ...  Generative Adversarial Networks Generative Adversarial Networks (GANs) include a generator and a discriminator [118] , where the generator aims to deceive the discriminator by synthesizing fake samples  ... 
arXiv:2102.09546v2 fatcat:iwzddzjy2rhunbuqz7h6tso5ii

A Survey of Learning on Small Data [article]

Xiaofeng Cao, Weixin Bu, Shengjun Huang, Yingpeng Tang, Yaming Guo, Yi Chang, Ivor W. Tsang
2022 arXiv   pre-print
This survey follows the agnostic active sampling under a PAC (Probably Approximately Correct) framework to analyze the generalization error and label complexity of learning on small data using a supervised  ...  However, there are few theoretical guarantees for their generalization performance.  ...  Spatio-temporal Graph Convolutional Networks (CGCN) [194] , Spatial-temporal Graph Convolutional Networks (ST-GCN) [195] and a serious of other variants follow the paradigm.  ... 
arXiv:2207.14443v1 fatcat:axtykeuv55hw7gczw2ezw6epni
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