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:10.48550/arxiv.2008.08903

Cited by 13 publications

(17 citation statements)

References 88 publications

(183 reference statements)

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“…In conclusion, we have seen that our trace generation method is able to produce novel, realistic network traffic traces that can easily be integrated in simulations as well as experiments in testbeds. double runTime = MassHelper(interfaceContainer, nodeContainer) .SetEpochTime (10.5) .SetProtocol("tcp") .SetMessageSize(2048) .SetMaxUpRate (5) .SetMaxDownRate(100) .EnableAppContext() .SetInitialApp("INTERACT") .SetStreamStayProbability(0.8) .SetInteractStayProbability(0.6) .SetTrace("/data/sample.trace") .SetClientIndex (3) .SetServerIndex(4) .Init(); Simulator::Stop (Seconds (runTime)); Simulator::Run (); Simulator::Destroy ();…”

Section: Discussionmentioning

confidence: 99%

“…Similar time series GANs have been proposed for music score generation in [4]. A survey of spatio-temporal GAN research is available in [5].…”

Section: Our Proposed Gan Modelmentioning

confidence: 99%

“…General properties of the adversarial framework in general, and GANs in particular, for implicitly learning probability distributions from a statistical point of view, are discussed and studied in [9]. A survey of GAN models for generation of spatio temporal data is available in [5], where the authors provide examples of GAN models to generate time series, spatio-temporal events, spatio-temporal graphs, and trajectory data.…”

Section: Related Workmentioning

confidence: 99%

“…In the latter case, more aggressive filtering of the traces in the ensuing collections may have to be performed, possibly involving not just the mean μ, but also the standard deviation σ and/or the skewness μ3 . Alternatively, the threshold for difference between the means computed over the traces assigned each context label may be set to be higher than the value of 10% that we set in the present work 5 . More discriminative splits of the training data across the different context labels will also lead to many context label trace collections failing the threshold of the minimum users with the minimum length sequences assigned that context label.…”

Section: Transfer Learning For Context-aware Ganmentioning

confidence: 99%

“…In the present work, in order for us to train a model there need to be at least 5 users with at least the configured sequence length number of data points (12 in our case) 5. The threshold may be treated as a hyperparameter that is optimized through an outer iterative loop as part of an AutoML workflow[13], but we do not elaborate on this here.…”

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confidence: 99%

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MASS: Mobile Autonomous Station Simulation

Sandholm¹,

Mukherjee²

2021

Preprint

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We propose a set of tools to replay wireless network traffic traces, while preserving the privacy of the original traces. Traces are generated by a user-and context-aware trained generative adversarial network (GAN).The replay allows for realistic traces from any number of users and of any trace duration to be produced given contextual parameters like the type of application and the real-time signal strength.We demonstrate the usefulness of the tools in three replay scenarios: Linux-and Androidstation experiments and NS3 simulations.We also evaluate the ability of the GAN model to generate traces that retain key statistical properties of the original traces such as feature correlation, statistical moments, and novelty.Our results show that we beat both traditional statistical distribution fitting approaches as well as a state-of-the-art GAN time series generator across these metrics. The ability of our GAN model to generate any number of user traces regardless of the number of users in the original trace also makes our tools more practically applicable compared to previous GAN approaches.Furthermore, we present a use case where our tools were employed in a Wi-Fi research experiment.

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Section: Discussionmentioning

confidence: 99%

“…Similar time series GANs have been proposed for music score generation in [4]. A survey of spatio-temporal GAN research is available in [5].…”

Section: Our Proposed Gan Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Transfer Learning For Context-aware Ganmentioning

confidence: 99%

mentioning

confidence: 99%

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MASS: Mobile Autonomous Station Simulation

Sandholm¹,

Mukherjee²

2021

Preprint

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Games of GANs: game-theoretical models for generative adversarial networks

et al. 2023

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Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game-theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative models and improve the GAN's performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, Games of GANs and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each class. Finally, we discuss the remaining challenges in this field and present future research directions.

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Evaluating the Impact of Health Care Data Completeness for Deep Generative Models

2023

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Background: Deep generative models (DGMs) present a promising avenue for generating realistic, synthetic data to augment existing healthcare datasets. However, exactly how the completeness of the original dataset affects the quality of the generated synthetic data is unclear. Objectives: In this paper, we investigate the effect of data completeness on samples generated by the most common DGM paradigms. Methods: We create both cross-sectional and panel datasets with varying missingness and subset rates and train generative adversarial networks (GANs), variational autoencoders (VAEs) and autoregressive models (Transformers) on these datasets. We then compare the distributions of generated data with original training data to measure similarity. Results: We find that increased incompleteness is directly correlated with increased dissimilarity between original and generated samples produced through DGMs. Conclusions: Care must be taken when using DGMs to generate synthetic data as data completeness issues can affect the quality of generated data in both panel and cross-sectional datasets.

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Generative Adversarial Networks for Spatio-temporal Data: A Survey

Cited by 13 publications

References 88 publications

MASS: Mobile Autonomous Station Simulation

MASS: Mobile Autonomous Station Simulation

Games of GANs: game-theoretical models for generative adversarial networks

Evaluating the Impact of Health Care Data Completeness for Deep Generative Models

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