Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Chuanguang Yang"'
Autor:
Chuanguang Yang
Publikováno v:
Archives of Civil Engineering, Vol vol. 70, Iss No 3, Pp 459-472 (2024)
To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of tradi
Externí odkaz:
https://doaj.org/article/c644b29665084284b87ec27df2012b92
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:140-148
We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF) framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the proposed PGMPF selectively suppresses the gradient of those ”unimportant” parameters via
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3f20133a5b8f199250be62ea358aadd
https://doi.org/10.1609/aaai.v36i1.19888
https://doi.org/10.1609/aaai.v36i1.19888
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9cf9bbbd952a80063db0fdd7b4ab5802
http://arxiv.org/abs/2207.11518
http://arxiv.org/abs/2207.11518
Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples. However, they ignore the global semantic relations among pixels across
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7bbc14be4fb922fd79d429434c3c7d98
Publikováno v:
Journal of Oncology.
Objectives. To detect the expression of circular RNA (circRNA) circINTS4 in triple-negative breast cancer (TNBC) and to analyze the relationship between the expression of circRNA circINTS4 and the clinicopathological characteristics and chemotherapy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6500da4d0a6a76b56ec045985318e74
Autor:
Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200526
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::044f717100d828c9da2c750ce86dc85e
https://doi.org/10.1007/978-3-031-20053-3_31
https://doi.org/10.1007/978-3-031-20053-3_31
Autor:
Ding Li, Tianyao Zheng, Rui Wu, Min Du, Ping Yao, Kai Deng, Renbiao Liu, Kun Zhang, Chuanguang Yang
Publikováno v:
HUMA @ ACM Multimedia
The target of 2D human pose estimation is to locate the keypoints of body parts from 2D images. State-of-the-art methods for pose estimation usually construct pixel-wise heatmaps from keypoints as labels for learning neural networks, which are usuall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b358a430e0e9a6ef8e4bd91e66f47a3b
https://doi.org/10.1145/3475723.3484252
https://doi.org/10.1145/3475723.3484252
Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to define and transfer knowledge. Previous KD methods often focus on mining
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b151c31c56c0eaed20954c0b04178002
http://arxiv.org/abs/2109.03075
http://arxiv.org/abs/2109.03075
Publikováno v:
IJCAI
Knowledge distillation often involves how to define and transfer knowledge from teacher to student effectively. Although recent self-supervised contrastive knowledge achieves the best performance, forcing the network to learn such knowledge may damag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84e77f412b083c2e54f2ce3932cc1708
https://doi.org/10.24963/ijcai.2021/168
https://doi.org/10.24963/ijcai.2021/168
Publikováno v:
IJCNN
Filter pruning is widely used to reduce the computation of deep learning, enabling the deployment of Deep Neural Networks (DNNs) in resource-limited devices. Conventional Hard Filter Pruning (HFP) method zeroizes pruned filters and stops updating the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d216f8eebc479ec86b468cf16d558080
https://doi.org/10.1109/ijcnn52387.2021.9534116
https://doi.org/10.1109/ijcnn52387.2021.9534116