Taking Class Imbalance Into Account in Open Set Recognition Evaluation
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917892368367616 |
|---|---|
| author | Komorniczak, Joanna Ksieniewicz, Pawel |
| author_facet | Komorniczak, Joanna Ksieniewicz, Pawel |
| contents | In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Recognition, focusing on the impact of class imbalance, especially in the dichotomy between known and unknown samples. As an outcome of problem analysis, we present a set of guidelines for evaluation of methods in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Taking Class Imbalance Into Account in Open Set Recognition Evaluation Komorniczak, Joanna Ksieniewicz, Pawel Machine Learning Computer Vision and Pattern Recognition In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Recognition, focusing on the impact of class imbalance, especially in the dichotomy between known and unknown samples. As an outcome of problem analysis, we present a set of guidelines for evaluation of methods in this field. |
| title | Taking Class Imbalance Into Account in Open Set Recognition Evaluation |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.06331 |