The focus of recent research has shifted from merely increasing the Deep
Neural Networks (DNNs) performance in various tasks to DNNs, which are more
interpretable to humans. The field of eXplainable Artificial Intelligence (XAI)
has observed various techniques, including saliency-based and concept-based
approaches. Concept-based approaches explain the model's decisions in simple
human understandable terms called Concepts. Concepts are human interpretable
units of data and are the thinking ground of humans. Explanations in terms of
concepts enable detecting spurious correlations, inherent biases, or
clever-hans. With the advent of concept-based explanations, there have been
various concept representation methods and automatic concept discovery
algorithms. Some recent methods use concepts for post-hoc model disentanglement
evaluation, while others use them for ante-hoc training. The concept-based
approaches are new, with many representations coming up, and there is very
limited work on Concept-based Model improvement. We provide a systematic review
and taxonomy of various concept representations and their discovery algorithms
in DNNs, specifically in vision. We also provide details on concept-based model
improvement literature, which is the first to survey concept-based model
improvement methods.

最近的研究关注点已经从简单地提高深度神经网络（DNNs）在各种任务中的性能转向了更具人可解释性的 DNNs。人工智能解释性领域已经观察到了包括基于显著性和基于概念的技术。概念是人类可理解的数据单元，是人类思考的基础。本文系统地回顾和分类了 DNNs 中各种概念表示及其发现算法，特别是在视觉领域。同时也提供了关于基于概念的模型改进文献的详细信息，这是第一个调查基于概念的模型改进方法的研究。

基于概念的模型改进方法综述

A survey on Concept-based Approaches For Model Improvement

In recent years, concept-based approaches have emerged as some of the most
promising explainability methods to help us interpret the decisions of
Artificial Neural Networks (ANNs). These methods seek to discover intelligible
visual 'concepts' buried within the complex patterns of ANN activations in two
key steps: (1) concept extraction followed by (2) importance estimation. While
these two steps are shared across methods, they all differ in their specific
implementations. Here, we introduce a unifying theoretical framework that
comprehensively defines and clarifies these two steps. This framework offers
several advantages as it allows us: (i) to propose new evaluation metrics for
comparing different concept extraction approaches; (ii) to leverage modern
attribution methods and evaluation metrics to extend and systematically
evaluate state-of-the-art concept-based approaches and importance estimation
techniques; (iii) to derive theoretical guarantees regarding the optimality of
such methods. We further leverage our framework to try to tackle a crucial
question in explainability: how to efficiently identify clusters of data points
that are classified based on a similar shared strategy. To illustrate these
findings and to highlight the main strategies of a model, we introduce a visual
representation called the strategic cluster graph. Finally, we present
this https URL, a dedicated website that offers a complete
compilation of these visualizations for all classes of the ImageNet dataset.

提出概念为基础的方法可帮助我们解释人工神经网络的决策，介绍了一个理论框架，可以定义和澄清概念提取和重要性评估两个步骤，并尝试解决解释性中的一个关键问题：如何有效地识别以类似共享策略分类的数据点组成的簇。