  Despite the great success of neural visual generative models in recent years,
integrating them with strong symbolic reasoning systems remains a challenging
task. There are two levels of symbol grounding problems among the core
challenges: the first is symbol assignment, i.e. mapping latent factors of
neural visual generators to semantic-meaningful symbolic factors from the
reasoning systems by learning from limited labeled data. The second is rule
learning, i.e. learning new rules that govern the generative process to enhance
the symbolic reasoning systems. To deal with these two problems, we propose a
neurosymbolic learning approach, Abductive visual Generation (AbdGen), for
integrating logic programming systems with neural visual generative models
based on the abductive learning framework. To achieve reliable and efficient
symbol grounding, the quantized abduction method is introduced for generating
abduction proposals by the nearest-neighbor lookup within semantic codebooks.
To achieve precise rule learning, the contrastive meta-abduction method is
proposed to eliminate wrong rules with positive cases and avoid less
informative rules with negative cases simultaneously. Experimental results show
that compared to the baseline approaches, AbdGen requires significantly less
labeled data for symbol assignment. Furthermore, AbdGen can effectively learn
underlying logical generative rules from data, which is out of the capability
of existing approaches. The code is released at this link:
https://github.com/candytalking/AbdGen.
