DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization.
Zheqi Lv|Wenqiao Zhang|Shengyu Zhang|Kun Kuang|Feng Wang|Yongwei Wang|Zhengyu Chen|Tao Shen|Hongxia Yang|Beng Chin Ooi|Fei Wu
| Anthology ID: | DBLP:conf/www/LvZ0KWW0SYO023 |
|---|---|
| Volume: | Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 |
| Year: | 2023 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 3077-3085 |
| URL: | https://doi.org/10.1145/3543507.3583451 |
| DOI: | https://doi.org/10.1145/3543507.3583451 |
| DBLP: | conf/www/LvZ0KWW0SYO023 |
| BibTeX: |
@inproceedings{lv-2023-duet,
author = {Zheqi Lv and
Wenqiao Zhang and
Shengyu Zhang and
Kun Kuang and
Feng Wang and
Yongwei Wang and
Zhengyu Chen and
Tao Shen and
Hongxia Yang and
Beng Chin Ooi and
Fei Wu},
editor = {Ying Ding and
Jie Tang and
Juan F. Sequeda and
Lora Aroyo and
Carlos Castillo and
Geert-Jan Houben},
title = {{DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization}},
booktitle = {{Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}},
pages = {3077--3085},
publisher = {ACM},
year = {2023},
url = {https://doi.org/10.1145/3543507.3583451},
doi = {https://doi.org/10.1145/3543507.3583451}
}