# LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding

arXiv: https://arxiv.org/abs/1906.06566 · HTML: https://mlsanity.com/paper/1906.06566/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information](https://mlsanity.com/paper/2104.06057/) (2021)
- [VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance](https://mlsanity.com/paper/2103.17003/) (2021)

## Abstract (arXiv)

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A drawback of building agnostic explanators is that the neighbourhood generation process is universal and consequently does not guarantee true adjacency between the generated neighbours and the instance. This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.