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Despite the great success of neural visual generative models in recent years,integrating them with strong symbolic knowledge reasoning systems remains achallenging task. The main challenges are two-fold: one is symbol assignment,i.e. bonding latent factors of neural visual generators with meaningful symbolsfrom knowledge reasoning systems. Another is rule learning, i.e. learning newrules, which govern the generative process of the data, to augment theknowledge reasoning systems. To deal with these symbol grounding problems, wepropose a neural-symbolic learning approach, Abductive Visual Generation(AbdGen), for integrating logic programming systems with neural visualgenerative models based on the abductive learning framework. To achievereliable and efficient symbol assignment, the quantized abduction method isintroduced for generating abduction proposals by the nearest-neighbor lookupswithin semantic codebooks. To achieve precise rule learning, the contrastivemeta-abduction method is proposed to eliminate wrong rules with positive casesand avoid less-informative rules with negative cases simultaneously.Experimental results on various benchmark datasets show that compared to thebaselines, AbdGen requires significantly fewer instance-level labelinginformation for symbol assignment. Furthermore, our approach can effectivelylearn underlying logical generative rules from data, which is out of thecapability of existing approaches.

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings