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

arXiv: https://arxiv.org/abs/2005.00065 · HTML: https://mlsanity.com/paper/2005.00065/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 30 of 65)

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- [A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model](https://mlsanity.com/paper/2507.00761/) (2025)
- [A Survey of the Self Supervised Learning Mechanisms for Vision Transformers](https://mlsanity.com/paper/2408.17059/) (2024)
- [Generalization Error of GAN from the Discriminator's Perspective](https://mlsanity.com/paper/2107.03633/) (2021)
- [Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning](https://mlsanity.com/paper/2111.01750/) (2021)
- [A Quantum Generative Adversarial Network for distributions](https://mlsanity.com/paper/2110.02742/) (2021)
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- [Data-driven Machinery Fault Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2405.18843/) (2024)
- [Leveraging 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 Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Evading Forensic Classifiers with Attribute-Conditioned Adversarial Faces](https://mlsanity.com/paper/2306.13091/) (2023)
- [An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry](https://mlsanity.com/paper/2401.05579/) (2024)
- [Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network](https://mlsanity.com/paper/2410.14965/) (2024)
- [FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis](https://mlsanity.com/paper/2412.06690/) (2024)
- [Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis](https://mlsanity.com/paper/2203.02855/) (2022)
- [Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models](https://mlsanity.com/paper/2307.16865/) (2023)
- [Combating Mode Collapse in GANs via Manifold Entropy Estimation](https://mlsanity.com/paper/2208.12055/) (2022)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Synthetic data generation method for data-free knowledge distillation in regression neural networks](https://mlsanity.com/paper/2301.04338/) (2023)
- [Generative Modeling of Microweather Wind Velocities for Urban Air Mobility](https://mlsanity.com/paper/2503.02690/) (2025)
- [MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution](https://mlsanity.com/paper/2206.12178/) (2022)
- [SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems](https://mlsanity.com/paper/2409.12328/) (2024)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Improving Novelty Detection using the Reconstructions of Nearest Neighbours](https://mlsanity.com/paper/2111.06150/) (2021)
- [Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue](https://mlsanity.com/paper/2203.11410/) (2022)
- [Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review](https://mlsanity.com/paper/2205.05493/) (2022)
- [Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis](https://mlsanity.com/paper/2305.11832/) (2023)

## 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.