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of 33
pro vyhledávání: '"Wang, Xinshao"'
Unsupervised domain adaption (UDA) is a transfer learning task where the data and annotations of the source domain are available but only have access to the unlabeled target data during training. Most previous methods try to minimise the domain gap b
Externí odkaz:
http://arxiv.org/abs/2211.08894
In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an $n$-dimensional signal $\mathbf{x}$ from $m$ i.i.d.~realizations of the single index model $y = f(\mathbf{a}^T\mathbf{x})
Externí odkaz:
http://arxiv.org/abs/2210.05571
Autor:
Wang, Xinshao, Hua, Yang, Kodirov, Elyor, Mukherjee, Sankha Subhra, Clifton, David A., Robertson, Neil M.
There is a family of label modification approaches including self and non-self label correction (LC), and output regularisation. They are widely used for training robust deep neural networks (DNNs), but have not been mathematically and thoroughly ana
Externí odkaz:
http://arxiv.org/abs/2207.00118
Autor:
Li, Ziyun, Wang, Xinshao, Hu, Di, Robertson, Neil M., Clifton, David A., Meinel, Christoph, Yang, Haojin
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under adverse conditions. For example, label noise usually leads to less reliable
Externí odkaz:
http://arxiv.org/abs/2106.01489
Publikováno v:
In Signal Processing: Image Communication January 2024 120
Publikováno v:
CVPR 2021
To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealin
Externí odkaz:
http://arxiv.org/abs/2005.03788
Akademický článek
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Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take advantage of structured similarity informati
Externí odkaz:
http://arxiv.org/abs/1911.09976
Set-based person re-identification (SReID) is a matching problem that aims to verify whether two sets are of the same identity (ID). Existing SReID models typically generate a feature representation per image and aggregate them to represent the set a
Externí odkaz:
http://arxiv.org/abs/1911.09143
Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach for training robust deep models under these adverse conditions. DM has
Externí odkaz:
http://arxiv.org/abs/1905.11233