Generative Adversarial Networks for Spatio-temporal Data: A Survey

Gao, Nan; Xue, Hao; Shao, Wei; Zhao, Sichen; Qin, Kyle Kai; Prabowo, Arian; Rahaman, Mohammad Saiedur; Salim, Flora D.

doi:10.1145/3474838

Cited by 67 publications

(31 citation statements)

References 135 publications

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“…LSTM‐based architecture has also been used in numerous recurrent GANs for pedestrian trajectory and medical time‐series generation (e.g. Esteban et al, 2017; Gao et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…In this context, embedding instances of data that share statistical properties. It has become a state-of-the-art approach to generate various types of data, such as image, audio and spatio-temporal data including human trajectories (Cao et al, 2019;Gao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…GANs consist in a pair of deep neural networks that aim to capture the data distribution of some experimental dataset, and that enable to generate new instances of data that share statistical properties. It has become a state‐of‐the‐art approach to generate various types of data, such as image, audio and spatio‐temporal data including human trajectories (Cao et al, 2019; Gao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Using generative adversarial networks (GAN) to simulate central‐place foraging trajectories

2022

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Miniature electronic devices have recently enabled ecologists to document relatively large amounts of animal trajectories. Modelling such trajectories may contribute to explaining the mechanisms underlying observed behaviours and to clarifying ecological processes at the scale of the population by simulating multiple trajectories. Existing approaches to animal movement modelling have mainly addressed the first objective, and are often limited when used for simulation purposes. Individual‐based models generally rely on ad hoc formulation and their empirical parametrization lacks generability, while random walks based on mathematically sound statistical inference typically consist of first‐order Markovian models calibrated at the local scale which may lead to overly simplistic description and simulation of animal trajectories. We investigate a recent deep learning tool—generative adversarial networks (GAN)—to simulate animal trajectories. GANs consist of a pair of deep neural networks that aim to capture the data distribution of some experimental dataset. They enable the generation of new instances of data that share statistical properties. This study aims at identifying relevant deep network architectures to simulate central‐place foraging trajectories, as well as at evaluating GANs drawbacks and benefits over classical methods, such as state‐switching hidden Markov models (HMM). We demonstrate the outstanding ability of deep convolutional GANs to simulate and to capture medium‐ to large‐scale properties of seabird foraging trajectories. GAN‐derived synthetic trajectories reproduced the Fourier spectral density of observed trajectories better than those simulated using HMMs. However, unlike HMMs, GANs do not adequately capture local‐scale descriptive statistics, such as step speed distributions. GANs provide a new likelihood‐free approach to calibrate complex stochastic processes and thus open new research avenues for animal movement modelling. We discuss the potential uses of GANs in movement ecology and future developments to better capture local‐scale features. In this context, embedding HMM‐based priors in GAN schemes appears as a promising research direction.

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“…LSTM‐based architecture has also been used in numerous recurrent GANs for pedestrian trajectory and medical time‐series generation (e.g. Esteban et al, 2017; Gao et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using generative adversarial networks (GAN) to simulate central‐place foraging trajectories

2022

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“…There are several comprehensive reviews on SSRL models based on Generative Adversarial Networks [57,93], autoencoders [61] and contrastive learning [76,78,88] in the fields of CV and NLP. However, none of those concentrates on either multimodal or temporal data.…”

Section: Related Surveysmentioning

confidence: 99%

Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data

Deldari¹,

Xue²,

Saeed³

et al. 2022

Preprint

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Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of modalities, including time series from sensors. The popularity of self-supervised learning is driven by the fact that traditional models typically require a huge amount of well-annotated data for training. Acquiring annotated data can be a difficult and costly process. Self-supervised methods have been introduced to improve the efficiency of training data through discriminative pre-training of models using supervisory signals that have been freely obtained from the raw data. Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.To this end, we 1) provide a comprehensive categorization of existing SSRL methods, 2) introduce a generic pipeline by defining the key components of a SSRL framework, 3) compare existing models in terms of their objective function, network architecture and potential applications, and 4) review existing multimodal techniques in each category and various modalities. Finally, we present existing weaknesses and future opportunities. We believe our work develops a perspective on the requirements of SSRL in domains that utilise multimodal and/or temporal data.

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“…Therefore, advanced privacy-preserving methods are imperative to addressing this emerging risk [295]. Recently, synthetic data-generation methods are also posing a threat to OSN users' privacy by creating data similar to real data [296,297]. Therefore, many privacypreserving approaches are needed to provide resilience against these threats.…”

Section: Promising Future Research Directionsmentioning

confidence: 99%

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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Generative Adversarial Networks for Spatio-temporal Data: A Survey

Cited by 67 publications

References 135 publications

Using generative adversarial networks (GAN) to simulate central‐place foraging trajectories

Using generative adversarial networks (GAN) to simulate central‐place foraging trajectories

Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

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