On the Transition from Neural Representation to Symbolic Knowledge
Junyan Cheng Peter Chin
Bridging the huge disparity between neural and symbolic representation can
potentially enable the incorporation of symbolic thinking into neural networks
from essence. Motivated by how human gradually builds complex symbolic
representation from the prototype symbols that are learned through perception
and environmental interactions. We propose a Neural-Symbolic Transitional
Dictionary Learning (TDL) framework that employs an EM algorithm to learn a
transitional representation of data that compresses high-dimension information
of visual parts of an input into a set of tensors as neural variables and
discover the implicit predicate structure in a self-supervised way. We
implement the framework with a diffusion model by regarding the decomposition
of input as a cooperative game, then learn predicates by prototype clustering.
We additionally use RL enabled by the Markovian of diffusion models to further
tune the learned prototypes by incorporating subjective factors. Extensive
experiments on 3 abstract compositional visual objects datasets that require
the model to segment parts without any visual features like texture, color, or
shadows apart from shape and 3 neural/symbolic downstream tasks demonstrate the
learned representation enables interpretable decomposition of visual input and
smooth adaption to downstream tasks which are not available by existing
methods.