Date of Submission
Spring 2021
Academic Program
Computer Science
Project Advisor 1
Sven Anderson
Abstract/Artist's Statement
Neural Networks, a form of machine learning, are used in increasingly important roles in the modern world. They are being used in self-driving cars and medical diagnoses. However, they are “Black Boxes”: they cannot be easily interpreted by humans. This project combines two methods of explaining a neural network’s decisions in an attempt to improve their accuracy. This new method, relevance-based testing with concept activation vectors (R-TCAV), yields promising results on two small experiments but is less precise than the previous TCAV method.
Open Access Agreement
Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Fischel, Henning, "Relevance-TCAV: Explaining Deep Neural Nets in Human Concepts" (2021). Senior Projects Spring 2021. 194.
https://digitalcommons.bard.edu/senproj_s2021/194
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