BNN-SAM: Improving generalization of binary object detector by Seeking Flat Minima-Reference-Cited by-同舟云学术

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BNN-SAM: Improving generalization of binary object detector by Seeking Flat Minima

Published:2024-04 Issue:8 Volume:54 Page:6682-6700
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Pu Han,Zhang Dezheng,Xu Ke,Mo RuChan,Yan ZhiHong,Wang Dong

Funder

National Key Research and Development Program of China

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05512-z.pdf

Reference45 articles.

1. Liu W, Anguelov D, Erhan D (2016) Ssd: Single shot multibox detector. Lecture Notes in Computer Science p 21-37

2. Zhu C, He Y, Savvides M (2019) Feature selective anchor-free module for single-shot object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp 840–849

3. Lin TY, Goyal P, Girshick RB et al (2020) Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 42:318–327

4. Molchanov P, Mallya A, Tyree S, et al (2019) Importance estimation for neural network pruning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11264–11272

5. Ye Y, You G, Fwu JK, et al (2020) Channel pruning via optimal thresholding. In: International Conference on Neural Information Processing, Springer, pp 508–516

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