Image classification is a primary task in data analysis where explainable
models are crucially demanded in various applications. Although amounts of
methods have been proposed to obtain explainable knowledge from the black-box
classifiers, these approaches lack the efficiency of extracting global
knowledge regarding the classification task, thus is vulnerable to local traps
and often leads to poor accuracy. In this study, we propose a generative
explanation model that combines the advantages of global and local knowledge
for explaining image classifiers. We develop a representation learning method
called class association embedding (CAE), which encodes each sample into a pair
of separated class-associated and individual codes. Recombining the individual
code of a given sample with altered class-associated code leads to a synthetic
real-looking sample with preserved individual characters but modified
class-associated features and possibly flipped class assignments. A
building-block coherency feature extraction algorithm is proposed that
efficiently separates class-associated features from individual ones. The
extracted feature space forms a low-dimensional manifold that visualizes the
classification decision patterns. Explanation on each individual sample can be
then achieved in a counter-factual generation manner which continuously
modifies the sample in one direction, by shifting its class-associated code
along a guided path, until its classification outcome is changed. We compare
our method with state-of-the-art ones on explaining image classification tasks
in the form of saliency maps, demonstrating that our method achieves higher
accuracies. The code is available at this https URL

图像分类是数据分析中的一个主要任务，其中需要关注解释性模型；本研究提出了一个结合全局和局部知识优势的生成式解释模型来解释图像分类器，通过一种表征学习方法实现了对每个样本的解释，并将其与现有方法进行了对比，结果表明该方法能够取得更高的准确度。

使用类别关联嵌入的图像分类器精确解释模型

Accurate Explanation Model for Image Classifiers using Class Association  Embedding

Explainability poses a major challenge to artificial intelligence (AI)
techniques. Current studies on explainable AI (XAI) lack the efficiency of
extracting global knowledge about the learning task, thus suffer deficiencies
such as imprecise saliency, context-aware absence and vague meaning. In this
paper, we propose the class association embedding (CAE) approach to address
these issues. We employ an encoder-decoder architecture to embed sample
features and separate them into class-related and individual-related style
vectors simultaneously. Recombining the individual-style code of a given sample
with the class-style code of another leads to a synthetic sample with preserved
individual characters but changed class assignment, following a cyclic
adversarial learning strategy. Class association embedding distills the global
class-related features of all instances into a unified domain with well
separation between classes. The transition rules between different classes can
be then extracted and further employed to individual instances. We then propose
an active XAI framework which manipulates the class-style vector of a certain
sample along guided paths towards the counter-classes, resulting in a series of
counter-example synthetic samples with identical individual characters.
Comparing these counterfactual samples with the original ones provides a
global, intuitive illustration to the nature of the classification tasks. We
adopt the framework on medical image classification tasks, which show that more
precise saliency maps with powerful context-aware representation can be
achieved compared with existing methods. Moreover, the disease pathology can be
directly visualized via traversing the paths in the class-style space.

本研究提出了一种基于类别关联嵌入的方法来进一步优化可解释人工智能（XAI），并在医学图像分类任务中证明其有效性，该方法利用了循环对抗性学习策略，通过生成具有相同个体特征但不同类别分配的中间类别样本，优化类别特征区分度，并从对立分类的路径中提取过渡规则以达到更好的可解释性。