Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method are introduced to enhance the controllability of logical symbols over generation. Furthermore, we propose two logical abduction methods to make our approach require few labeled training data and support the induction of latent logical generative rules from data. We experimentally show that our approach can be utilized to integrate various neural generative models with logical reasoning systems, by both learning from scratch or utilizing pre-trained models directly. The code is released at https://github.com/future-item/AbdGen.
@article{arxiv.2310.17451,
title = {Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings},
author = {Yifei Peng and Zijie Zha and Yu Jin and Zhexu Luo and Wang-Zhou Dai and Zhong Ren and Yao-Xiang Ding and Kun Zhou},
journal= {arXiv preprint arXiv:2310.17451},
year = {2025}
}