3 May 2017 An approach to explainable deep learning using fuzzy inference
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Bonanno, David Bonanno, Kristen Nock, Kristen Nock, Leslie Smith, Leslie Smith, Paul Elmore, Paul Elmore, Fred Petry, Fred Petry, } "An approach to explainable deep learning using fuzzy inference", Proc. SPIE 10207, Next-Generation Analyst V, 102070D (3 May 2017); doi: 10.1117/12.2268001; https://doi.org/10.1117/12.2268001

Back to Top