Towards Explainable Neural-Symbolic Visual Reasoning - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year :

Towards Explainable Neural-Symbolic Visual Reasoning

Abstract

Many high-performance models suffer from a lack of interpretability. There has been an increasing influx of work on explainable artificial intelligence (XAI) in order to disentangle what is meant and expected by XAI. Nevertheless, there is no general consensus on how to produce and judge explanations. In this paper, we discuss why techniques integrating connectionist and symbolic paradigms are the most efficient solutions to produce explanations for non-technical users and we propose a reasoning model, based on definitions by Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision. We use this explanation in order to correct bias in the network's decision rationale. We accompany this model with an example of its potential use, based on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).

Dates and versions

hal-02379596 , version 1 (25-11-2019)

Identifiers

Cite

Adrien Bennetot, Jean-Luc Laurent, Raja Chatila, Natalia Díaz-Rodríguez. Towards Explainable Neural-Symbolic Visual Reasoning. 2019. ⟨hal-02379596⟩
141 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More