A Human-In-the-Loop System for Interpreting Image Recognition Models

Master thesis (2022)

Authors

A.H. Ziengs Electrical Engineering, Mathematics and Computer Science

Contributors

J. Yang Web Information Systems - EEMCS (mentor)
G.J.P.M. Houben Web Information Systems - EEMCS (mentor)
Luis Cruz Software Engineering - EEMCS (graduation committee member)
N. Yorke-Smith Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Bart Ziengs
More Info
expand_more

Published Date

01-06-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Interpretability of ML models and image recognition models specifaclly, is a increasing problem. In this thesis, the design and implementation of Brickroutine: a system that used a trained model, is presented. Using human annotations, semantic interpretations are given to image classification problems. By giving an iterative approach in terms of workflows and technically designing it in a modular, salable way using Docker, the authors aim for bridging the gap between performance and interpretability of AI by combining human and computational intelligence.