This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-layer of a CNN for object classification usually represents a mixture of object parts. We develop a simple yet effective method to disentangle object-part pattern components from each filter. We construct an explanatory graph to organize the mined part patterns, where a node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More crucially, given a pre-trained CNN, the explanatory graph is learned without a need of annotating object parts. Experiments show that each graph node consistently represented the same object part through different images, which boosted the transferability of CNN features. We transferred part patterns in the explanatory graph to the task of part localization, and our method significantly outperformed other approaches.

翻译：本文引入了一个图形模型, 即一个解释性图解, 它揭示了在受过训练的CNN 的电离层内隐藏的知识等级。 每个在CNN目标分类的电流层中的过滤器通常代表一个物体的组合。 我们开发了一个简单而有效的方法, 将每个过滤器的物体部分图案组件分解出来。 我们构造了一个解释性图解, 用于组织被开采部分的图案, 其中节点代表一个部分图案, 每个边缘编码 共激活关系和模式间的空间关系。 更重要的是, 如果经过训练的CNN CNN, 解释性图解无需说明对象部分。 实验显示, 每个图案的节点通过不同的图像始终代表同一个对象部分, 这增加了CNN CN 特性的可传输性。 我们将解释性图案中的一部分图案转换到局部化的任务, 我们的方法大大优于其他方法 。