Summary of Towards Robust Training Datasets For Machine Learning with Ontologies: a Case Study For Emergency Road Vehicle Detection, by Lynn Vonderhaar et al.
Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection
by Lynn Vonderhaar, Timothy Elvira, Tyler Procko, Omar Ochoa
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve trust in Machine Learning (ML) models used in safety-critical domains such as autonomous driving. The black box nature of ML can make it difficult to rely on these models without human experts verifying each decision. To address this, the authors suggest ensuring the robustness and completeness of the training dataset by utilizing domain ontologies and image quality characteristic ontologies. These ontologies help validate the domain completeness and image quality robustness of the training data, thereby increasing trust in ML model decisions. The paper also presents a proof-of-concept experiment for this method, building ontologies for the emergency road vehicle domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists aim to make Machine Learning (ML) models more reliable in critical areas like self-driving cars. Right now, it’s hard to trust these models because they work behind closed doors. To fix this, researchers suggest making sure the training data is good quality and complete. They propose using special dictionaries called ontologies to check if the data covers all the important aspects of a domain (like emergency vehicles) and if the images are high-quality. This can help people trust ML models more. The team shows how this works by building ontologies for emergency road vehicle domain. |
Keywords
» Artificial intelligence » Machine learning
