CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929314024390656 |
|---|---|
| author | Truong, Thanh-Dat Helton, Pierce Moustafa, Ahmed Cothren, Jackson David Luu, Khoa |
| author_facet | Truong, Thanh-Dat Helton, Pierce Moustafa, Ahmed Cothren, Jackson David Luu, Khoa |
| contents | Although unsupervised domain adaptation methods have achieved remarkable performance in semantic scene segmentation in visual perception for self-driving cars, these approaches remain impractical in real-world use cases. In practice, the segmentation models may encounter new data that have not been seen yet. Also, the previous data training of segmentation models may be inaccessible due to privacy problems. Therefore, to address these problems, in this work, we propose a Continual Unsupervised Domain Adaptation (CONDA) approach that allows the model to continuously learn and adapt with respect to the presence of the new data. Moreover, our proposed approach is designed without the requirement of accessing previous training data. To avoid the catastrophic forgetting problem and maintain the performance of the segmentation models, we present a novel Bijective Maximum Likelihood loss to impose the constraint of predicted segmentation distribution shifts. The experimental results on the benchmark of continual unsupervised domain adaptation have shown the advanced performance of the proposed CONDA method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_00621 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars Truong, Thanh-Dat Helton, Pierce Moustafa, Ahmed Cothren, Jackson David Luu, Khoa Computer Vision and Pattern Recognition Although unsupervised domain adaptation methods have achieved remarkable performance in semantic scene segmentation in visual perception for self-driving cars, these approaches remain impractical in real-world use cases. In practice, the segmentation models may encounter new data that have not been seen yet. Also, the previous data training of segmentation models may be inaccessible due to privacy problems. Therefore, to address these problems, in this work, we propose a Continual Unsupervised Domain Adaptation (CONDA) approach that allows the model to continuously learn and adapt with respect to the presence of the new data. Moreover, our proposed approach is designed without the requirement of accessing previous training data. To avoid the catastrophic forgetting problem and maintain the performance of the segmentation models, we present a novel Bijective Maximum Likelihood loss to impose the constraint of predicted segmentation distribution shifts. The experimental results on the benchmark of continual unsupervised domain adaptation have shown the advanced performance of the proposed CONDA method. |
| title | CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2212.00621 |