Summary of Taking Class Imbalance Into Account in Open Set Recognition Evaluation, by Joanna Komorniczak and Pawel Ksieniewicz
Taking Class Imbalance Into Account in Open Set Recognition Evaluation
by Joanna Komorniczak, Pawel Ksieniewicz
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper presents a comprehensive evaluation of Deep Neural Network-based systems for Open Set Recognition, which is crucial for increasing user trust. The study focuses on the impact of class imbalance, particularly between known and unknown samples. By analyzing various methods, the authors provide guidelines for evaluating Open Set Recognition models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well AI systems can handle new situations they haven’t seen before. These systems are popular but often get confused when they don’t know what to do with something that’s not in their training data. The researchers studied different ways to solve this problem and found some guidelines for making sure these models work correctly. |
Keywords
* Artificial intelligence * Neural network
