论文标题
Sointer：一种新型的基于深层的解释方法，用于解释结构化输出模型
SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models
论文作者
论文摘要
我们提出了一种新颖的解释技术来解释结构化输出模型的行为，该模型同时学习输入向量之间的映射到一组输出变量。由于结构化模型中输出变量的计算路径之间存在复杂的关系，因此功能可以影响输出的价值。我们将重点放在一个输出作为目标上，并尝试找到结构化模型使用的最重要的功能来决定输入空间每个地方的目标。在本文中，我们假设一个任意结构化的输出模型可作为黑匣子使用，并认为考虑输出变量之间的相关性如何改善解释性能。目标是训练输入空间上目标输出变量的解释器功能。我们引入了一个基于能量的培训过程，该过程有效地考虑了将要解释的模型中纳入的结构信息。使用多种模拟和实际数据集确认了所提出方法的有效性。
We propose a novel interpretation technique to explain the behavior of structured output models, which learn mappings between an input vector to a set of output variables simultaneously. Because of the complex relationship between the computational path of output variables in structured models, a feature can affect the value of output through other ones. We focus on one of the outputs as the target and try to find the most important features utilized by the structured model to decide on the target in each locality of the input space. In this paper, we assume an arbitrary structured output model is available as a black box and argue how considering the correlations between output variables can improve the explanation performance. The goal is to train a function as an interpreter for the target output variable over the input space. We introduce an energy-based training process for the interpreter function, which effectively considers the structural information incorporated into the model to be explained. The effectiveness of the proposed method is confirmed using a variety of simulated and real data sets.
