Rubio-Manzano, C.; Segura-Navarrete, A.; Martinez-Araneda, C.; Vidal-Castro, C. Explainable Hopfield Neural Networks Using an Automatic Video-Generation System. Appl. Sci.2021, 11, 5771.
Rubio-Manzano, C.; Segura-Navarrete, A.; Martinez-Araneda, C.; Vidal-Castro, C. Explainable Hopfield Neural Networks Using an Automatic Video-Generation System. Appl. Sci. 2021, 11, 5771.
Rubio-Manzano, C.; Segura-Navarrete, A.; Martinez-Araneda, C.; Vidal-Castro, C. Explainable Hopfield Neural Networks Using an Automatic Video-Generation System. Appl. Sci.2021, 11, 5771.
Rubio-Manzano, C.; Segura-Navarrete, A.; Martinez-Araneda, C.; Vidal-Castro, C. Explainable Hopfield Neural Networks Using an Automatic Video-Generation System. Appl. Sci. 2021, 11, 5771.
Abstract
Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. Their main feature is their ability 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 their execution. This work constitutes a novel approach to get 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. Finally, we apply our approach for creating a complete explainer video about the execution of HNNs on a small recognition problem.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
