论文标题
使用可视化解码基于CNN的对象分类器
Decoding CNN based Object Classifier Using Visualization
论文作者
论文摘要
本文研究了如何在自动驾驶汽车的机器感知的背景下通过可视化来解释卷积神经网络（CNN）的工作。我们可以看到CNN的不同卷积层提取了哪些类型的特征，这有助于了解CNN如何逐渐增加每一层中的空间信息。因此，它集中在每个转化中的兴趣区域。可视化激活的热图有助于我们了解CNN如何在图像中分类和定位不同对象。这项研究还可以帮助我们推理模型较低精度的背后，有助于增加对对象检测模块的信任。
This paper investigates how working of Convolutional Neural Network (CNN) can be explained through visualization in the context of machine perception of autonomous vehicles. We visualize what type of features are extracted in different convolution layers of CNN that helps to understand how CNN gradually increases spatial information in every layer. Thus, it concentrates on region of interests in every transformation. Visualizing heat map of activation helps us to understand how CNN classifies and localizes different objects in image. This study also helps us to reason behind low accuracy of a model helps to increase trust on object detection module.
