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

• URL: http://arxiv.org/abs/2012.12899v2
• Date: Thu, 11 Mar 2021 01:05:48 GMT
• Title: Learning by Self-Explanation, with Application to Neural Architecture Search
• Authors: Ramtin Hosseini, Pengtao Xie
• Abstract summary: 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. We apply LeaSE for neural architecture search on CIFAR-100, CIFAR-10, and ImageNet.
• Score: 12.317568257671427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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