Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures,including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations.

翻译：可解释人工智能（XAI）正逐渐成为众多AI应用的关键组成部分。然而，基于规则和修正反向传播的XAI方法在应用于包含创新层构建模块的现代模型架构时，常面临挑战，这源于两个原因。首先，基于规则的XAI方法具有高度灵活性，导致大量潜在参数化方案。其次，许多XAI方法因难以处理某些模型组件（例如批量归一化层）而违反实现不变性公理。后者可通过模型规范化来解决，即在不改变底层功能的前提下重组模型以忽略有问题的组件。虽然对于简单架构（如VGG、ResNet）而言模型规范化较为直接，但对于更复杂且高度互联的模型（如DenseNet）则可能具有挑战性。此外，目前缺乏量化证据证明模型规范化对XAI有益。在本工作中，我们提出了适用于当前主流深度神经网络架构（包括VGG、ResNet、EfficientNet、DenseNet及关系网络）中相关模型块的规范化方法。我们进一步提出了一种XAI评估框架，在Pascal-VOC和ILSVRC2017数据集的图像分类任务以及使用CLEVR-XAI的视觉问答任务中，量化并比较了模型规范化对各种XAI方法的影响。此外，针对上述第一个问题，我们展示了如何应用该评估框架对XAI方法进行超参数搜索，以优化解释质量。