An approach to explainable deep learning using fuzzy inference

David Bonanno; Kristen Nock; Leslie Smith; Paul Elmore; Fred Petry

doi:10.1117/12.2268001

3 May 2017 An approach to explainable deep learning using fuzzy inference

David Bonanno, Kristen Nock, Leslie Smith, Paul Elmore, Fred Petry

Proceedings Volume 10207, Next-Generation Analyst V; 102070D (2017) https://doi.org/10.1117/12.2268001
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Convolutional neural networks continue to dominate image classification problems and recursive neural networks have proven their utility in caption generation and language translations. While these approaches are powerful, they do not offer explanation for how the output is generated. Without understanding how deep learning arrives at a solution there is no guarantee that these networks will transition from controlled laboratory environments to fieldable systems. This paper presents an approach for incorporating such rule based methodology into neural networks by embedding fuzzy inference systems into deep learning networks.

Citation Download Citation

David Bonanno, Kristen Nock, Leslie Smith, Paul Elmore, and Fred Petry "An approach to explainable deep learning using fuzzy inference", Proc. SPIE 10207, Next-Generation Analyst V, 102070D (3 May 2017); https://doi.org/10.1117/12.2268001

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