# Learning to Explain with Complemental Examples

arXiv: https://arxiv.org/abs/1812.01280 · HTML: https://mlsanity.com/paper/1812.01280/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [Human Attention in Fine-grained Classification](https://mlsanity.com/paper/2111.01628/) (2021)
- [CoRTX: Contrastive Framework for Real-time Explanation](https://mlsanity.com/paper/2303.02794/) (2023)
- [Efficient XAI Techniques: A Taxonomic Survey](https://mlsanity.com/paper/2302.03225/) (2023)
- [Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching](https://mlsanity.com/paper/2102.03919/) (2021)
- [TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators](https://mlsanity.com/paper/2302.09561/) (2023)
- [Extracting human interpretable structure-property relationships in chemistry using XAI and large language models](https://mlsanity.com/paper/2311.04047/) (2023)
- [Explainable AI for Natural Adversarial Images](https://mlsanity.com/paper/2106.09106/) (2021)
- [Information-theoretic Evolution of Model Agnostic Global Explanations](https://mlsanity.com/paper/2105.06956/) (2021)
- [Abstraction, Validation, and Generalization for Explainable Artificial Intelligence](https://mlsanity.com/paper/2105.07508/) (2021)
- [Explainable AI Approach using Near Misses Analysis](https://mlsanity.com/paper/2411.16895/) (2024)
- [Can Multitask Learning Enhance Model Explainability?](https://mlsanity.com/paper/2508.06966/) (2025)

## Abstract (arXiv)

This paper addresses the generation of explanations with visual examples. Given an input sample, we build a system that not only classifies it to a specific category, but also outputs linguistic explanations and a set of visual examples that render the decision interpretable. Focusing especially on the complementarity of the multimodal information, i.e., linguistic and visual examples, we attempt to achieve it by maximizing the interaction information, which provides a natural definition of complementarity from an information theoretical viewpoint. We propose a novel framework to generate complemental explanations, on which the joint distribution of the variables to explain, and those to be explained is parameterized by three different neural networks: predictor, linguistic explainer, and example selector. Explanation models are trained collaboratively to maximize the interaction information to ensure the generated explanation are complemental to each other for the target. The results of experiments conducted on several datasets demonstrate the effectiveness of the proposed method.