ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present ScrutinAI, a Visual Analytics approach to exploit semantic understanding for deep neural network (DNN) predictions analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g. autonomous driving or healthcare, have a high demand for detecting and mitigating data- and model-inherent shortcomings. Our approach aims to help analysts use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI therefore includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, and data filtering with textual queries on descriptive meta data. The tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the ''black-box character'' of the DNN and thus support model improvement and generation of a safety argumentation for AI applications. We present a case study on the investigation of DNN models for pedestrian detection from the automotive domain.
Description
CCS Concepts: Human-centered computing --> Visual analytics; Interactive systems and tools; Computing methodologies --> Neural networks; Object detection
@inproceedings{10.2312:eurova.20221071,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Bernard, Jürgen and Angelini, Marco},
title = {{ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions}},
author = {Haedecke, Elena and Mock, Michael and Akila, Maram},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},
DOI = {10.2312/eurova.20221071}
}