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Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions

arXiv 2005.00065 ↗First posted 30 Apr 2020cs.LG eess.IV stat.MLcited by 65

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Abstract (arXiv)

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 14; 2023: 11; 2024: 11; 2025: 16; 2026: 1.

65 papers.

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  1. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  2. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  3. 2022Accelerating Diffusion Sampling with Classifier-based Feature Distillationcited by 15
  4. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
  5. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  6. 2025A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Modelcited by 10
  7. 2021Generalization Error of GAN from the Discriminator's Perspectivecited by 8
  8. 2021Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learningcited by 8
  9. 2024A Survey of the Self Supervised Learning Mechanisms for Vision Transformerscited by 8
  10. 2021A Quantum Generative Adversarial Network for distributionscited by 7
  11. 2022Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEscited by 7
  12. 2024Data-driven Machinery Fault Diagnosis: A Comprehensive Reviewcited by 7
38 more
  1. 2024Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancementcited by 7
  2. 2023Evading Forensic Classifiers with Attribute-Conditioned Adversarial Facescited by 6
  3. 2024An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometrycited by 6
  4. 2024Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Networkcited by 6
  5. 2024FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesiscited by 6
  6. 2022Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysiscited by 5
  7. 2023Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Modelscited by 5
  8. 2022Combating Mode Collapse in GANs via Manifold Entropy Estimationcited by 4
  9. 20223D Brain and Heart Volume Generative Models: A Surveycited by 4
  10. 2023Synthetic data generation method for data-free knowledge distillation in regression neural networkscited by 4
  11. 2025Generative Modeling of Microweather Wind Velocities for Urban Air Mobilitycited by 3
  12. 2022MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distributioncited by 2
  13. 2024SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problemscited by 2
  14. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  15. 2021Improving Novelty Detection using the Reconstructions of Nearest Neighbourscited by 1
  16. 2022Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issuecited by 1
  17. 2022Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Reviewcited by 1
  18. 2023Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysiscited by 1
  19. 2023Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networkscited by 1
  20. 2024Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoisingcited by 1
  21. 2024Consumer Transactions Simulation through Generative Adversarial Networkscited by 1
  22. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiogramscited by 1
  23. 2025Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscalingcited by 1
  24. 2025AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolutioncited by 1
  25. 2025Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Networkcited by 1
  26. 2025Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Financecited by 1
  27. 2025Tumor Synthesis conditioned on Radiomicscited by 1
  28. 2025Precipitation nowcasting of satellite data using physically-aligned neural networkscited by 1
  29. 2021Training face verification models from generated face identity datacited by 0
  30. 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
  31. 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
  32. 2021Automatic Feature Highlighting in Noisy RES Data With CycleGANcited by 0
  33. 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
  34. 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
  35. 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
  36. 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
  37. 2022Using Graph Neural Networks for Program Terminationcited by 0
  38. 2023Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networkscited by 0

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