Hierarchical feature learning based on convolutional neural networks (CNN)
has recently shown significant potential in various computer vision tasks.
While allowing high-quality discriminative feature learning, the downside of
CNNs is the lack of explicit structure in features, which often leads to
overfitting, absence of reconstruction from partial observations and limited
generative abilities. Explicit structure is inherent in hierarchical
compositional models, however, these lack the ability to optimize a
well-defined cost function. We propose a novel analytic model of a basic unit
in a layered hierarchical model with both explicit compositional structure and
a well-defined discriminative cost function. Our experiments on two datasets
show that the proposed compositional model performs on a par with standard CNNs
on discriminative tasks, while, due to explicit modeling of the structure in
the feature units, affording a straight-forward visualization of parts and
faster inference due to separability of the units. Actions

本文提出了一种基于层次化组合模型的分析模型，通过显式的结构模型和明确的鉴别成本函数结合，在两个数据集上的实验表明，该模型在判别任务中表现和标准 CNN 相当，同时由于特征单元中结构的明确建模，使得其部分具体化可视化、推理快速分离，并且从局部观察中还原能力更强。