Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Huang, Zenan"'
Channel attention mechanisms endeavor to recalibrate channel weights to enhance representation abilities of networks. However, mainstream methods often rely solely on global average pooling as the feature squeezer, which significantly limits the over
Externí odkaz:
http://arxiv.org/abs/2403.01713
The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. The Automated Model Evaluation (AutoEval) shows an alternative to this t
Externí odkaz:
http://arxiv.org/abs/2401.12689
Distributional shift between domains poses great challenges to modern machine learning algorithms. The domain generalization (DG) signifies a popular line targeting this issue, where these methods intend to uncover universal patterns across disparate
Externí odkaz:
http://arxiv.org/abs/2310.03007
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a.k.a.}, latent processes identification. Driven by the independent assumption, existing works have made great progre
Externí odkaz:
http://arxiv.org/abs/2305.08164
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this pap
Externí odkaz:
http://arxiv.org/abs/2301.00383
Autor:
Wei, Taotao, Cai, Hongyue, Zhang, Xiandong, Yang, Juanjuan, Huang, Zenan, Sun, Shujing, Duan, Tingting, Shi, Miaomiao, Tu, Tieyao, Qian, Xin
Publikováno v:
In Fungal Ecology August 2024 70
Autor:
Huang, Zenan, Sun, Yixuan, Liu, Sijian, Chen, Xiaopeng, Ping, Junyu, Fei, Peng, Gong, Zhefeng, Zheng, Nenggan
Publikováno v:
In Biochemical and Biophysical Research Communications 1 October 2024 727
Autor:
Wen, Jun, Shui, Changjian, Kuang, Kun, Yuan, Junsong, Huang, Zenan, Gong, Zhefeng, Zheng, Nenggan
Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability through domain-invariance learning. However, source-learned discriminability it
Externí odkaz:
http://arxiv.org/abs/2011.03737
Publikováno v:
Jisuanji kexue, Vol 50, Iss 1, Pp 229-242 (2023)
With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of app
Externí odkaz:
https://doaj.org/article/352f0577cb114b6a8ec4da4fc61338a6
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