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.

翻译：单体神经网络采用单一权重集学习下游任务的有用表征，明确忽略了数据生成过程中的组合性本质。该特性存在于每项实例均可视为身份概念（如物体形状）与修饰概念（如方向、颜色和尺寸）组合而成的数据中。对组合性的忽视在机器人领域尤为不利，因为状态估计高度依赖物理机制（如旋转和变换）的组合性本质来建模交互关系。为适应这一数据特性，模块化网络被提出。然而，由于各模块角色缺乏明确结构，以及模块坍缩等模块化网络特有的问题，其可用性受到限制。我们提出了一种模块化网络架构，通过预设模块角色的独特结构来适配上述分解性概念。同时，我们提供了正则化方法，在提升模型分解精度的同时增强了模块化网络对模块坍缩问题的鲁棒性。