# xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

arXiv: https://arxiv.org/abs/2002.10438 · HTML: https://mlsanity.com/paper/2002.10438/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement](https://mlsanity.com/paper/2203.08008/) (2022)
- [Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans](https://mlsanity.com/paper/2310.19427/) (2023)
- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach](https://mlsanity.com/paper/2407.11771/) (2024)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions](https://mlsanity.com/paper/2401.10217/) (2024)
- [Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey](https://mlsanity.com/paper/2111.13282/) (2021)
- [Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation](https://mlsanity.com/paper/2308.12053/) (2023)
- [xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network](https://mlsanity.com/paper/2306.15760/) (2023)
- [Editable Stain Transformation Of Histological Images Using Unpaired GANs](https://mlsanity.com/paper/2312.03647/) (2023)
- [Applications of Explainable artificial intelligence in Earth system science](https://mlsanity.com/paper/2406.11882/) (2024)
- [VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces](https://mlsanity.com/paper/2408.02140/) (2024)
- [XMOL: Explainable Multi-property Optimization of Molecules](https://mlsanity.com/paper/2409.07786/) (2024)
- [What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models](https://mlsanity.com/paper/2504.20687/) (2025)
- [Refining Visual Artifacts in Diffusion Models via Explainable AI-based Flaw Activation Maps](https://mlsanity.com/paper/2512.08774/) (2025)
- [Counterfactual Edits for Generative Evaluation](https://mlsanity.com/paper/2303.01555/) (2023)
- [An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning](https://mlsanity.com/paper/2501.10698/) (2025)
- [Relevance-driven Input Dropout: an Explanation-guided Regularization Technique](https://mlsanity.com/paper/2505.21595/) (2025)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) are a revolutionary class of Deep Neural Networks (DNNs) that have been successfully used to generate realistic images, music, text, and other data. However, GAN training presents many challenges, notably it can be very resource-intensive. A potential weakness in GANs is that it requires a lot of data for successful training and data collection can be an expensive process. Typically, the corrective feedback from discriminator DNNs to generator DNNs (namely, the discriminator's assessment of the generated example) is calculated using only one real-numbered value (loss). By contrast, we propose a new class of GAN we refer to as xAI-GAN that leverages recent advances in explainable AI (xAI) systems to provide a "richer" form of corrective feedback from discriminators to generators. Specifically, we modify the gradient descent process using xAI systems that specify the reason as to why the discriminator made the classification it did, thus providing the "richer" corrective feedback that helps the generator to better fool the discriminator. Using our approach, we observe xAI-GANs provide an improvement of up to 23.18% in the quality of generated images on both MNIST and FMNIST datasets over standard GANs as measured by Frechet Inception Distance (FID). We further compare xAI-GAN trained on 20% of the data with standard GAN trained on 100% of data on the CIFAR10 dataset and find that xAI-GAN still shows an improvement in FID score. Further, we compare our work with Differentiable Augmentation - which has been shown to make GANs data-efficient - and show that xAI-GANs outperform GANs trained on Differentiable Augmentation. Moreover, both techniques can be combined to produce even better results. Finally, we argue that xAI-GAN enables users greater control over how models learn than standard GANs.