Generative Adversarial Networks for Spatio-temporal Data: A Survey

Abstract

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

arXiv commentThis paper has been accepted by ACM Transactions on Intelligent Systems and Technology (TIST)

References in corpus (37)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Semi-Supervised Classification with Graph Convolutional Networks
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Generative Adversarial Network in Medical Imaging: A Review
• Toward Multimodal Image-to-Image Translation
• Generating Videos with Scene Dynamics
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• How Generative Adversarial Networks and Their Variants Work: An Overview
• Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Mode Regularized Generative Adversarial Networks
• Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
• NetGAN: Generating Graphs via Random Walks
• Adversarial Ranking for Language Generation
• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
• Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
• EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
• Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
• Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
• Spatio-Temporal Data Mining: A Survey of Problems and Methods
• Towards an Integrated Approach to Crowd Analysis and Crowd Synthesis: a Case Study and First Results
• TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
• ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
• Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
• SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
• D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction
• Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
• COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
• Transfer Learning for Thermal Comfort Prediction in Multiple Cities
• Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

Cited by in corpus (11)

• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• A Systematic Survey of Regularization and Normalization in GANs
• Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
• Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
• Improving the quality of generative models through Smirnov transformation
• Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity
• Neural Click Models for Recommender Systems
• Game of GANs: Game-Theoretical Models for Generative Adversarial Networks
• Auto-encoding GPS data to reveal individual and collective behaviour
• Adversarial Machine Learning in Text Analysis and Generation
• MASS: Mobile Autonomous Station Simulation