RD-NAS: Enhancing One-Shot Supernet Ranking Ability Via Ranking Distillation From Zero-Cost Proxies
Author:
Affiliation:
1. National University of Defense Technology,School of Computer,Hunan,China
2. Chinese Academy of Sciences,Beijing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10094709.pdf?arnumber=10094709
Reference23 articles.
1. Teacher-free Distillation via Regularizing Intermediate Representation
2. Norm: Knowledge distillation via n-to-one representation matching;xiaolong;ICLRE,2022
3. Estimates of the Regression Coefficient Based on Kendall's Tau
4. Autoslim: Towards one-shot architecture search for channel numbers;yu;arXiv Computer Vision and Pattern Recognition,2019
5. Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
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1. Automated Knowledge Distillation via Monte Carlo Tree Search;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01
2. Review of Recent Distillation Studies;MATEC Web of Conferences;2023
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