论文标题
通过自我解释来学习，并应用神经体系结构搜索
Learning by Self-Explanation, with Application to Neural Architecture Search
论文作者
论文摘要
通过自我解释的学习是人类学习中的一种有效的学习技术，学生向自己解释一个学习的话题，以加深对这个话题的理解。研究人类广泛使用的这种解释驱动的学习方法是否也有助于改善机器学习，这很有趣。基于这一灵感，我们提出了一种新型的机器学习方法，称为自我解释（租赁）。在我们的方法中，解释器模型通过试图清楚地向受众模型解释有关如何做出预测结果，从而提高了学习能力。租赁被称为四级优化问题，涉及四个学习阶段的顺序，这些阶段是在统一框架中端到端进行的：1）解释者学习； 2）解释器解释； 3）观众学习； 4）根据观众的表现，解释器重新学习。我们开发了一种有效的算法来解决租赁问题。我们将租赁应用于CIFAR-100，CIFAR-10和Imagenet上的神经体系结构搜索。实验结果强烈证明了我们方法的有效性。
Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this topic. It is interesting to investigate whether this explanation-driven learning methodology broadly used by humans is helpful for improving machine learning as well. Based on this inspiration, we propose a novel machine learning method called learning by self-explanation (LeaSE). In our approach, an explainer model improves its learning ability by trying to clearly explain to an audience model regarding how a prediction outcome is made. LeaSE is formulated as a four-level optimization problem involving a sequence of four learning stages which are conducted end-to-end in a unified framework: 1) explainer learns; 2) explainer explains; 3) audience learns; 4) explainer re-learns based on the performance of the audience. We develop an efficient algorithm to solve the LeaSE problem. We apply LeaSE for neural architecture search on CIFAR-100, CIFAR-10, and ImageNet. Experimental results strongly demonstrate the effectiveness of our method.
