Logic Tensor Network-Enhanced Generative Adversarial Network

• URL: http://arxiv.org/abs/2601.03839v1
• Date: Wed, 07 Jan 2026 12:04:49 GMT
• Title: Logic Tensor Network-Enhanced Generative Adversarial Network
• Authors: Nijesh Upreti, Vaishak Belle,
• Abstract summary: We introduce Logic Network-Enhanced Generative Adversarial Network (LTN-GAN), a novel framework that enhances Generative Adversarial Networks (GANs)<n>We evaluate LTN-GAN across multiple datasets, including synthetic datasets (gaussian, grid, rings) and the MNIST dataset.<n>This work highlights the potential of neuro-symbolic approaches to enhance generative modeling in knowledge-intensive domains.
• Score: 2.3791444696448085
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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