Explainable Hopfield Neural Networks Using an Automatic Video-Generation System

Applied Sciences

Claudia Martinez-Araneda¹  Alejandra Segura-Navarrete²  Clemente Rubio-Manzano²  Christian Vidal-Castro² 

[1] Computer Science Department, Universidad Catolica de la Santísima Concepción, Concepción 4090541, Chile;Department of Information Systems, University of the Bío-Bío, Avda. Collao 1202, Casilla 5-C, Concepción 4051381, Chile;

关键词: explainable artificial intelligence;    hopfield neural networks;    automatic video generation;    data-to-text systems;    software visualization;   

DOI  :  10.3390/app11135771

来源: DOAJ

【 摘 要 】

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).

【 授权许可】

Unknown