论文标题
可视化黑框图像分类模型的色彩显着性
Visualizing Color-wise Saliency of Black-Box Image Classification Models
论文作者
论文摘要
通常使用基于机器学习的图像分类。但是，包括深度学习在内的高级方法给出的分类结果通常很难解释。这种可解释性问题是在安全至关重要系统中部署训练有素的模型的主要障碍之一。已经提出了几种解决这个问题的技术。其中之一是上升，解释了由热图产生的分类，称为显着图，该图解释了每个像素的重要性。我们提出了MC-Rise（多色上升），这是提高颜色信息在解释中考虑的一种增强。我们的方法不仅显示给定图像中每个像素的显着性，就像原始上升一样，而且每个像素的颜色成分的意义；带有颜色信息的显着性图在颜色信息很重要的域（例如，交通符号识别）很有用。我们使用两个数据集（GTSRB和ImageNet）实施了MC-RISE并评估它们，以证明我们的方法的有效性与现有技术来解释图像分类结果相比。
Image classification based on machine learning is being commonly used. However, a classification result given by an advanced method, including deep learning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, which explains the significance of each pixel. We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation. Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel; a saliency map with color information is useful especially in the domain where the color information matters (e.g., traffic-sign recognition). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.
