In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional neural networks (CNNs) particularly have demonstrated state-of-the-art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain the reasoning behind a prediction made by a network. We propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, our work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, which assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.

中文翻译：

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用于解释图像分类任务中卷积神经网络的白盒方法回顾

近年来，深度学习在解决来自多个领域的应用程序方面变得普遍。卷积神经网络 (CNN) 尤其在图像分类任务中展示了最先进的性能。然而，这些网络做出的决定并不透明，不能由人类直接解释。已经提出了几种方法来解释网络做出的预测背后的推理。我们提出了一种基于假设和实现对这些方法进行分组的拓扑。我们主要关注利用网络内部架构信息来解释其决策的白盒方法。给定图像分类任务和经过训练的 CNN，我们的工作旨在提供一组可用于为特定图像创建解释图的方法的全面详细概述，这些方法根据图像对网络决策的贡献为图像的每个像素分配重要性分数。我们还提出了基于白盒方法的进一步分类，以实现更好的比较并帮助研究人员找到最适合不同场景的方法。