论文标题
C-SENN：对比度自我解释神经网络
C-SENN: Contrastive Self-Explaining Neural Network
论文作者
论文摘要
在这项研究中，我们使用一个自我解释的神经网络（SENN），该神经网络学习了无监督的概念，以获取人们易于自动理解的概念。在概念学习中，隐藏的层保留了与输出相关的可口理功能，这在适应需要解释的现实环境时至关重要。但是，众所周知，在一般环境中，例如自主驾驶场景，Senn的概念的解释性降低。因此，这项研究将对比度学习与概念学习结合在一起，以提高概念的可读性和任务的准确性。我们称此模型对比度自我解释神经网络（C-SENN）。
In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically. In concept learning, the hidden layer retains verbalizable features relevant to the output, which is crucial when adapting to real-world environments where explanations are required. However, it is known that the interpretability of concepts output by SENN is reduced in general settings, such as autonomous driving scenarios. Thus, this study combines contrastive learning with concept learning to improve the readability of concepts and the accuracy of tasks. We call this model Contrastive Self-Explaining Neural Network (C-SENN).
