ML Anthology

Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More

Jingwen Ye, Yixin Ji, Xinchao Wang, Kairi Ou, Dapeng Tao, Mingli Song
CVPR 2019
doi:10.1109/CVPR.2019.00294 /cvpr/2019/ye2019cvpr-student/

Abstract

In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates the knowledge and masters the expertise of two pre-trained teacher models working on heterogeneous problems, one on scene parsing and the other on depth estimation. To this end, we propose an innovative training strategy that learns the parameters of the student intertwined with the teachers, achieved by "projecting" its amalgamated features onto each teacher's domain and computing the loss. We also introduce two options to generalize the proposed training strategy to handle three or more tasks simultaneously. The proposed scheme yields very encouraging results. As demonstrated on several benchmarks, the trained student model achieves results even superior to those of the teachers in their own expertise domains and on par with the state-of-the-art fully supervised models relying on human-labelled annotations.

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Cite

Text

Ye et al. "Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00294

Markdown

[Ye et al. "Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/ye2019cvpr-student/) doi:10.1109/CVPR.2019.00294

BibTeX

@inproceedings{ye2019cvpr-student,
  title     = {{Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More}},
  author    = {Ye, Jingwen and Ji, Yixin and Wang, Xinchao and Ou, Kairi and Tao, Dapeng and Song, Mingli},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00294},
  url       = {https://mlanthology.org/cvpr/2019/ye2019cvpr-student/}
}