Layer-wise Relevance Propagation (LRP) is an emerging and widely-used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a “best” LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance-based visual analysis with two important functions: relevance-based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

中文翻译：

---

分层相关性传播模型的可视化设计器

逐层相关性传播 (LRP) 是一种新兴且广泛使用的方法，用于解释卷积神经网络 (CNN) 的预测结果。LRP 开发人员经常选择和采用不同的相关性反向传播规则和参数，以计算输入图像的相关性分数。然而，没有明显的解决方案来定义“最佳”LRP 模型。一个满意的模型高度依赖于相关的图像和设计师的目标。我们开发了一个可视化模型设计器，名为 VisLRPDesigner，以克服 LRP 模型设计和使用中的挑战。各种 LRP 规则统一到一个集成框架中，具有直观的参数设置工作流程。VisLRPDesigner 因此允许用户交互式配置和比较 LRP 模型。它还通过两个重要功能促进基于相关性的视觉分析：基于相关性的像素翻转和神经元消融。几个用例说明了 VisLRPDesigner 的好处。视觉设计器的可用性和局限性由 LRP 用户评估。