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Akademický článek
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Akademický článek
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Autor:
Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Harchaoui, Warith, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frédéric
In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely
Externí odkaz:
http://arxiv.org/abs/2309.02858
Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source domain data that share a common label space but follow a different distribution. Most of the recent methods take the approach
Externí odkaz:
http://arxiv.org/abs/2302.11984
Autor:
Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frédéric
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While feature selection algorithms are often model-based through optimised m
Externí odkaz:
http://arxiv.org/abs/2302.03391
Autor:
Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Harchaoui, Warith, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frederic
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely
Externí odkaz:
http://arxiv.org/abs/2210.06300
Publikováno v:
In Manufacturing Letters August 2023 35 Supplement:1072-1080
Autor:
Zhang, Hongjing, Davidson, Ian
Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as $k$-means and spectral clustering. However, this strong representation learning ability may
Externí odkaz:
http://arxiv.org/abs/2105.14146
Autor:
Zhai, Yunpeng, Lu, Shijian, Ye, Qixiang, Shan, Xuebo, Chen, Jie, Ji, Rongrong, Tian, Yonghong
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown. Existing methods attempt to address this challenge by transferring image styles or aligning feature distributions
Externí odkaz:
http://arxiv.org/abs/2004.08787
Publikováno v:
In Pattern Recognition July 2022 127