Summary of Label Convergence: Defining An Upper Performance Bound in Object Recognition Through Contradictory Annotations, by David Tschirschwitz et al.
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations
by David Tschirschwitz, Volker Rodehorst
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A machine learning study addresses the issue of annotation errors in model training and evaluation. The problem arises from label variations and inaccuracies in datasets, leading to inconsistent examples that deviate from established labeling conventions. This inconsistency can significantly impact model performance on metrics like mean Average Precision (mAP). To tackle this challenge, the researchers introduce the concept of “label convergence,” which defines an upper bound on model accuracy when faced with contradictory test annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models struggle to achieve optimal performance due to inconsistent labeling in datasets. This issue affects both training and evaluation phases. The study proposes a new approach called “label convergence” that helps identify the highest achievable performance under these challenging conditions. |
Keywords
» Artificial intelligence » Machine learning » Mean average precision
