Evaluating variational autoencoder methods for out-of-distribution detection in autonomous vehicles
In a safety-critical system like autonomous vehicles, it is essential to ensure that the observations shown are within the distribution of training data, otherwise they are called out-of-distribution (OOD). OOD detection is a fundamental problem that needs to be addressed to avoid errors in image re...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/166524 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In a safety-critical system like autonomous vehicles, it is essential to ensure that the observations shown are within the distribution of training data, otherwise they are called out-of-distribution (OOD). OOD detection is a fundamental problem that needs to be addressed to avoid errors in image recognition tasks, especially in real-time system. Variational autoencoder (VAE) has emerged as the most promising method to address this issue. Several modifications have been made to VAE to improve its performance, especially in terms of increasing disentanglement, yet no research has been done to evaluate its performance on OOD detection. In this research, four VAE variants were tested on a traffic dataset to see which one gives the best results. After which, the relationship between disentanglement and OOD detection is evaluated. |
|---|