Despite the great success of neural visual generative models in recent years,
integrating them with strong symbolic knowledge reasoning systems remains a
challenging task. The main challenges are two-fold: one is symbol assignment,
i.e. bonding latent factors of neural visual generators with meaningful symbols
from knowledge reasoning systems. Another is rule learning, i.e. learning new
rules, which govern the generative process of the data, to augment the
knowledge reasoning systems. To deal with these symbol grounding problems, we
propose a neural-symbolic 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 assignment, the quantized abduction method is
introduced for generating abduction proposals by the nearest-neighbor lookups
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 on various benchmark datasets show that compared to the
baselines, AbdGen requires significantly fewer instance-level labeling
information for symbol assignment. Furthermore, our approach can effectively
learn underlying logical generative rules from data, which is out of the
capability of existing approaches.

通过引入量化的逆向推理方法和对比性元逆推理方法，提出了一种神经符号学习方法 AbdGen，用于将逻辑编程系统与神经视觉生成模型集成，解决了符号赋值和规则学习的挑战，实验证明相比基线方法，AbdGen 在符号赋值上需要较少的实例级标记信息，并可以有效地从数据中学习潜在的逻辑生成规则。

通过理解生成：带有逻辑符号基础的神经视觉生成

Generating by Understanding: Neural Visual Generation with Logical  Symbol Groundings

Perception and reasoning are basic human abilities that are seamlessly
connected as part of human intelligence. However, in current machine learning
systems, the perception and reasoning modules are incompatible. Tasks requiring
joint perception and reasoning ability are difficult to accomplish autonomously
and still demand human intervention. Inspired by the way language experts
decoded Mayan scripts by joining two abilities in an abductive manner, this
paper proposes the abductive learning framework. The framework learns
perception and reasoning simultaneously with the help of a trial-and-error
abductive process. We present the Neural-Logical Machine as an implementation
of this novel learning framework. We demonstrate that--using human-like
abductive learning--the machine learns from a small set of simple hand-written
equations and then generalizes well to complex equations, a feat that is beyond
the capability of state-of-the-art neural network models. The abductive
learning framework explores a new direction for approaching human-level
learning ability.

本研究提出基于 “缺省推理” 思想的联合感知和推理框架来实现人工智能的人类水平认知能力，并建立了神经逻辑机来实现该框架。实验证明，该框架在从少量手写公式数据中学习后，能够很好地推广到复杂的新数据中，并在人工智能领域中标志着开创了一种新的研究方向。