Monolithic neural networks that make use of a single set of weights to learn
useful representations for downstream tasks explicitly dismiss the
compositional nature of data generation processes. This characteristic exists
in data where every instance can be regarded as the combination of an identity
concept, such as the shape of an object, combined with modifying concepts, such
as orientation, color, and size. The dismissal of compositionality is
especially detrimental in robotics, where state estimation relies heavily on
the compositional nature of physical mechanisms (e.g., rotations and
transformations) to model interactions. To accommodate this data
characteristic, modular networks have been proposed. However, a lack of
structure in each module's role, and modular network-specific issues such as
module collapse have restricted their usability. We propose a modular network
architecture that accommodates the mentioned decompositional concept by
proposing a unique structure that splits the modules into predetermined roles.
Additionally, we provide regularizations that improve the resiliency of the
modular network to the problem of module collapse while improving the
decomposition accuracy of the model.

该论文探讨神经网络的组成特性以及地位，提出了一种基于模块化的网络结构，利用预定的角色将模块拆分，同时提供对模块崩溃问题的解决方案，提高了整个网络的鲁棒性和解析准确性。