Interpreting the decision logic behind effective deep convolutional neural
networks (CNN) on images complements the success of deep learning models.
However, the existing methods can only interpret some specific decision logic
on individual or a small number of images. To facilitate human
understandability and generalization ability, it is important to develop
representative interpretations that interpret common decision logics of a CNN
on a large group of similar images, which reveal the common semantics data
contributes to many closely related predictions. In this paper, we develop a
novel unsupervised approach to produce a highly representative interpretation
for a large number of similar images. We formulate the problem of finding
representative interpretations as a co-clustering problem, and convert it into
a submodular cost submodular cover problem based on a sample of the linear
decision boundaries of a CNN. We also present a visualization and similarity
ranking method. Our extensive experiments demonstrate the excellent performance
of our method.

本文提出一种基于无监督学习的方法来解释大量相似图像中深度卷积神经网络的决策逻辑，并将代表性解释问题转化为一种共聚类问题，并基于线性决策边界的样本将其转化为子模代价覆盖问题。我们还提供了可视化和相似性排序方法，广泛的实验证明了本方法的优秀表现。

卷积神经网络上的代表性解释发现

Finding Representative Interpretations on Convolutional Neural Networks

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications
generates a strong demand for explanations that are robust to noise and align
well with human intuition. Most existing methods generate explanations by
identifying a subgraph of an input graph that has a strong correlation with the
prediction. These explanations are not robust to noise because independently
optimizing the correlation for a single input can easily overfit noise.
Moreover, they do not align well with human intuition because removing an
identified subgraph from an input graph does not necessarily change the
prediction result. In this paper, we propose a novel method to generate robust
counterfactual explanations on GNNs by explicitly modelling the common decision
logic of GNNs on similar input graphs. Our explanations are naturally robust to
noise because they are produced from the common decision boundaries of a GNN
that govern the predictions of many similar input graphs. The explanations also
align well with human intuition because removing the set of edges identified by
an explanation from the input graph changes the prediction significantly.
Exhaustive experiments on many public datasets demonstrate the superior
performance of our method.

提出了一种能够生成对于噪音稳健且符合人类直觉的图神经网络的可靠反事实解释的新方法，该方法明确地对相似输入图的常见决策逻辑进行建模，并从许多相似输入图的公共决策边界中产生可靠的解释。