Learning by Self-Explanation, with Application to Neural Architecture Search

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.