User: Guest  Login
Title:

Leveraging Semantic Embeddings for Safety-Critical Applications

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Brunner, Thomas; Diehl, Frederik; Truong Le, Michael; Knoll, Alois
Pages contribution:
1389-1394
Abstract:
Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning. We observe their interpretability and discuss their potential utility in a safety-critical context. Concretely, we propose to use them to add introspection and error detection capabilities to neural network classifiers. First, we show how to create embeddings from symbolic domain knowledge. We discuss how to use them for interpreting mispredictions and propose a simple error detection scheme. We then...     »
Book / Congress title:
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Congress (additional information):
Long Beach, CA, USA
Publisher:
IEEE
Year:
2019
Month:
Jun
E-ISBN:
978-1-7281-2506-0
Reviewed:
ja
Fulltext / DOI:
doi:10.1109/CVPRW.2019.00179
Notes:
Oral (spotlight) presentation at the Workshop for Safe Artificial Intelligence for Automated Driving.
Copyright statement:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
 BibTeX