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

arXiv: https://arxiv.org/abs/2012.12899 · HTML: https://mlsanity.com/paper/2012.12899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Learning by Examples Based on Multi-level Optimization](https://mlsanity.com/paper/2109.10824/) (2021)
- [Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy](https://mlsanity.com/paper/2304.00486/) (2023)

## Abstract (arXiv)

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.