In this paper, we introduce a new approach, called "Posthoc Interpretation
via Quantization (PIQ)", for interpreting decisions made by trained
classifiers. Our method utilizes vector quantization to transform the
representations of a classifier into a discrete, class-specific latent space.
The class-specific codebooks act as a bottleneck that forces the interpreter to
focus on the parts of the input data deemed relevant by the classifier for
making a prediction. We evaluated our method through quantitative and
qualitative studies and found that PIQ generates interpretations that are more
easily understood by participants to our user studies when compared to several
other interpretation methods in the literature.

本文提出了一种名为 “通过量化实现的事后解释（PIQ）” 的新方法，用于解释训练分类器所做的决策。通过向量量化将分类器的表示转换为离散的、类特定的潜在空间，类特定的编码簿起到瓶颈作用，迫使解释器专注于被分类器认为与做出预测相关的输入数据的部分。我们通过定量和定性研究评估了我们的方法，并发现当与文献中其他几种解释方法进行比较时，PIQ 生成的解释更容易被我们用户研究的参与者理解。