We propose a new way to explain and to visualize neural network
classification through a decomposition-based explainable AI (DXAI). Instead of
providing an explanation heatmap, our method yields a decomposition of the
image into class-agnostic and class-distinct parts, with respect to the data
and chosen classifier. Following a fundamental signal processing paradigm of
analysis and synthesis, the original image is the sum of the decomposed parts.
We thus obtain a radically different way of explaining classification. The
class-agnostic part ideally is composed of all image features which do not
posses class information, where the class-distinct part is its complementary.
This new visualization can be more helpful and informative in certain
scenarios, especially when the attributes are dense, global and additive in
nature, for instance, when colors or textures are essential for class
distinction. Code is available at this https URL

提出了一种新的神经网络分类的解释和可视化方法，通过分解为类不可知和类不同的部分来解释和可视化图像，不同于热力图的解释方法，更有助于在某些场景中提供丰富和信息量大的分类解释。

DXAI：通过图像分解解释分类

DXAI: Explaining Classification by Image Decomposition

In this paper, we propose a graph classification approach for automatically
determining whether to use a monolithic or a decomposition-based solution
method. In this approach, an optimization problem is represented as a graph
that captures the structural and functional coupling among the variables and
constraints of the problem via an appropriate set of features. Given this
representation, a graph classifier is built to determine the best solution
method for a given problem. The proposed approach is used to develop a
classifier that determines whether a convex Mixed Integer Nonlinear Programming
problem should be solved using branch and bound or the outer approximation
algorithm. Finally, it is shown how the learned classifier can be incorporated
into existing mixed integer optimization solvers.

本文提出了一种基于图分类的方法，用于自动确定是使用整体式还是分解式的解决方案方法；通过适当的特征集，将优化问题表示为捕捉问题的变量和约束之间的结构和功能耦合的图；构建图分类器以确定给定问题的最佳解决方法；以解决分析凸混合整数非线性规划问题时使用分支定界法还是外逼近算法为例展示了所提出方法的应用；最后，展示了学习到的分类器如何并入现有的混合整数优化求解器中。

利用人工智能实现基于分解的优化算法自动化：第一部分，学习何时进行分解

Taking the human out of decomposition-based optimization via artificial  intelligence: Part I. Learning when to decompose

Graph Networks are used to make decisions in potentially complex scenarios
but it is usually not obvious how or why they made them. In this work, we study
the explainability of Graph Network decisions using two main classes of
techniques, gradient-based and decomposition-based, on a toy dataset and a
chemistry task. Our study sets the ground for future development as well as
application to real-world problems.

探讨利用基于梯度和分解两种主要技术在一个玩具数据集和化学任务上提高 Graph Networks 决策的可解释性，为未来的开发和真实世界问题的应用奠定了基础。