CoProNN：用于解释视觉模型的基于概念的原型最近邻方法
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models
AI总结：
本文提出CoProNN，一种通过文本到图像方法生成视觉概念原型并利用k近邻例程解释计算机视觉模型预测的模块化方法，在人机协作和细粒度任务中表现出色。
摘要：
人工智能可解释性（XAI）研究中越来越多的证据表明，好的解释应当针对单个任务进行定制，并与任务相关的概念相关联。然而，构建特定于任务的解释非常耗时，且需要难以整合到通用XAI方法中的领域专业知识。一种与领域专家共同设计有用的特定任务解释的有前景的方法，是基于语义概念的组合性。在此，我们提出了一种新颖的方法，使领域专家能够通过自然语言直观地快速为计算机视觉任务创建基于概念的解释。利用深度生成方法的最新进展，我们提出通过文本到图像方法生成基于视觉概念的原型。然后，这些原型通过简单的k近邻（k-Nearest-Neighbors）例程用于解释计算机视觉模型的预测。CoProNN的模块化设计实现简单，易于适应新任务，并允许在发布更强大模型时替换分类和文本到图像模型。该方法可以针对预定义原型的真实标签进行离线评估，这些原型也易于与领域专家沟通，因为它们基于视觉概念。我们展示了我们的策略在粗粒度图像分类任务上与其他基于概念的XAI方法相比具有很强竞争力，甚至在更具挑战性的细粒度任务上可能优于这些方法。我们在定性和定量用户研究中展示了我们的方法在人机协作环境中的有效性。所有代码和实验数据可以在我们的GitHub存储库中找到。
英文摘要：
Mounting evidence in explainability for artificial intelligence (XAI) research suggests that good explanations should be tailored to individual tasks and should relate to concepts relevant to the task. However, building task specific explanations is time consuming and requires domain expertise which can be difficult to integrate into generic XAI methods. A promising approach towards designing useful task specific explanations with domain experts is based on compositionality of semantic concepts. Here, we present a novel approach that enables domain experts to quickly create concept-based explanations for computer vision tasks intuitively via natural language. Leveraging recent progress in deep generative methods we propose to generate visual concept-based prototypes via text-to-image methods. These prototypes are then used to explain predictions of computer vision models via a simple k-Nearest-Neighbors routine. The modular design of CoProNN is simple to implement, it is straightforward to adapt to novel tasks and allows for replacing the classification and text-to-image models as more powerful models are released. The approach can be evaluated offline against the ground-truth of predefined prototypes that can be easily communicated also to domain experts as they are based on visual concepts. We show that our strategy competes very well with other concept-based XAI approaches on coarse grained image classification tasks and may even outperform those methods on more demanding fine grained tasks. We demonstrate the effectiveness of our method for human-machine collaboration settings in qualitative and quantitative user studies. All code and experimental data can be found in our GitHub $\href{https://github.com/TeodorChiaburu/beexplainable}{repository}$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
