Logic Tensor Network-Enhanced Generative Adversarial Network

TL;DR

This paper presents LTN-GAN, a novel generative model that combines GANs with Logic Tensor Networks to enforce logical constraints during data generation, improving logical consistency and diversity of samples.

Contribution

The paper introduces LTN-GAN, integrating LTNs with GANs to incorporate logical constraints into the generative process, a novel neuro-symbolic approach.

Findings

Outperforms traditional GANs in logical adherence

Maintains high quality and diversity of generated data

Effective across multiple datasets including synthetic and MNIST

Abstract

In this paper, we introduce Logic Tensor Network-Enhanced Generative Adversarial Network (LTN-GAN), a novel framework that enhances Generative Adversarial Networks (GANs) by incorporating Logic Tensor Networks (LTNs) to enforce domain-specific logical constraints during the sample generation process. Although GANs have shown remarkable success in generating realistic data, they often lack mechanisms to incorporate prior knowledge or enforce logical consistency, limiting their applicability in domains requiring rule adherence. LTNs provide a principled way to integrate first-order logic with neural networks, enabling models to reason over and satisfy logical constraints. By combining the strengths of GANs for realistic data synthesis with LTNs for logical reasoning, we gain valuable insights into how logical constraints influence the generative process while improving both the diversity and logical consistency of the generated samples. We evaluate LTN-GAN across multiple datasets, including synthetic datasets (gaussian, grid, rings) and the MNIST dataset, demonstrating that our model significantly outperforms traditional GANs in terms of adherence to predefined logical constraints while maintaining the quality and diversity of generated samples. This work highlights the potential of neuro-symbolic approaches to enhance generative modeling in knowledge-intensive domains.