Leveraging Semantic Embeddings for Safety-Critical Applications
| Autor: | Thomas Brunner, Alois Knoll, Frederik Diehl, Michael Truong Le |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre Semantics 01 natural sciences Machine Learning (cs.LG) Knowledge-based systems Semantic similarity Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Interpretability Artificial neural network business.industry ddc Task analysis Domain knowledge 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) |
| Zdroj: | CVPR Workshops |
| Popis: | 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 introduce the concept of semantic distance: a real-valued score that measures confidence in the semantic space. We evaluate this score on a traffic sign classifier and find that it achieves near state-of-the-art performance, while being significantly faster to compute than other confidence scores. Our approach requires no changes to the original network and is thus applicable to any task for which domain knowledge is available. Accepted at CVPR 2019 Workshop: Safe Artificial Intelligence for Automated Driving |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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