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In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views.
Training products of experts by minimizing contrastive divergence,
G. E. Hinton, · 2002
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching.,
A. Hyvärinen, P. Dayan, · 2005
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks,
G. E. Hinton, R. R. Salakhutdinov, · 2006
Earlier work this paper cites.
Visualizing data using t-sne.,
L. Van der Maaten, G. Hinton, · 2008
Earlier work this paper cites.
Deep belief networks based voice activity detection,
X.-L. Zhang, J. Wu, · 2012
Earlier work this paper cites.
Multi-view clustering via joint nonnegative matrix factorization,
J. Liu, C. Wang, J. Gao, J. Han, · 2013
Earlier work this paper cites.
Multi-view k-means clustering on big data,
X. Cai, F. Nie, H. Huang, · 2013
Earlier work this paper cites.
Boosted deep neural networks and multi-resolution cochleagram features for voice activity detection,
X.-L. Zhang, D. Wang, · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
Earlier work this paper cites.
Diversity-induced multi-view subspace clustering,
X. Cao, C. Zhang, H. Fu, S. Liu, H. Zhang, · 2015
Earlier work this paper cites.
Boosting contextual information for deep neural network based voice activity detection,
X.-L. Zhang, D. Wang, · 2015
Earlier work this paper cites.
A deep ensemble learning method for monaural speech separation,
X.-L. Zhang, D. Wang, · 2016
Earlier work this paper cites.
Multi-view clustering via deep matrix factorization,
H. Zhao, Z. Ding, Y. Fu, · 2017
Earlier work this paper cites.
Incomplete multi-view clustering via graph regularized matrix factorization,
J. Wen, Z. Zhang, Y. Xu, Z. Zhong, · 2018
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization,
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, Y. Bengio, · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding,
A. Van den Oord, Y. Li, O. Vinyals, et al., · 2018
Earlier work this paper cites.
Self-supervised video hashing with hierarchical binary auto-encoder,
J. Song, H. Zhang, X. Li, L. Gao, M. Wang, R. Hong, · 2018
Earlier work this paper cites.
Multiple kernel clustering with neighbor-kernel subspace segmentation,
S. Zhou, X. Liu, M. Li, E. Zhu, L. Liu, C. Zhang, J. Yin, · 2019
Earlier work this paper cites.
Comic: Multi-view clustering without parameter selection,
X. Peng, Z. Huang, J. Lv, H. Zhu, J. T. Zhou, · 2019
Earlier work this paper cites.
Deep adversarial multi-view clustering network.,
Z. Li, Q. Wang, Z. Tao, Q. Gao, Z. Yang, et al., · 2019
Earlier work this paper cites.
Deep adversarial multi-view clustering network.,
Z. Li, Q. Wang, Z. Tao, Q. Gao, Z. Yang, et al., · 2019
Earlier work this paper cites.
Ae2-nets: Autoencoder in autoencoder networks,
C. Zhang, Y. Liu, H. Fu, · 2019
Earlier work this paper cites.
Multi-view clustering in latent embedding space,
M.-S. Chen, L. Huang, C.-D. Wang, D. Huang, · 2020
Earlier work this paper cites.
Efficient and effective regularized incomplete multi-view clustering,
X. Liu, M. Li, C. Tang, J. Xia, J. Xiong, L. Liu, M. Kloft, E. Zhu, · 2020
Cited alongside, same era.
Subspace segmentation-based robust multiple kernel clustering,
S. Zhou, E. Zhu, X. Liu, T. Zheng, Q. Liu, J. Xia, J. Yin, · 2020
Cited alongside, same era.
Adaptive weighted graph fusion incomplete multi-view subspace clustering,
P. Zhang, S. Wang, J. Hu, Z. Cheng, X. Guo, E. Zhu, Z. Cai, · 2020
Cited alongside, same era.
End-to-end adversarial-attention network for multi-modal clustering,
R. Zhou, Y.-D. Shen, · 2020
Cited alongside, same era.
Auto-weighted multi-view clustering via deep matrix decomposition,
S. Huang, Z. Kang, Z. Xu, · 2020
Cited alongside, same era.
End-to-end adversarial-attention network for multi-modal clustering,
R. Zhou, Y.-D. Shen, · 2020
Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering,
J. Xu, Y. Ren, H. Tang, X. Pu, X. Zhu, M. Zeng, L. He, · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction,
J. Zbontar, L. Jing, I. Misra, Y. LeCun, S. Deny, · 2021
Later among the works it cites.
Graph contrastive clustering,
H. Zhong, J. Wu, C. Chen, J. Huang, M. Deng, L. Nie, Z. Lin, X.-S. Hua, · 2021
Later among the works it cites.
Deep embedded multi-view clustering with collaborative training,
J. Xu, Y. Ren, G. Li, L. Pan, C. Zhu, Z. Xu, · 2021
Later among the works it cites.
Fast incomplete multi-view clustering with view-independent anchors,
S. Liu, X. Liu, S. Wang, X. Niu, E. Zhu, · 2022
Later among the works it cites.
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Cited alongside, same era.
Contrastive multiview coding,
Y. Tian, D. Krishnan, P. Isola, · 2020
Cited alongside, same era.
Large-scale multi-view subspace clustering in linear time,
Z. Kang, W. Zhou, Z. Zhao, J. Shao, M. Han, Z. Xu, · 2020
Cited alongside, same era.
Tensor-svd based graph learning for multi-view subspace clustering,
Q. Gao, W. Xia, Z. Wan, D. Xie, P. Zhang, · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations,
T. Chen, S. Kornblith, M. Norouzi, G. Hinton, · 2020
Cited alongside, same era.
Bootstrap your own latent: A new approach to self-supervised learning,
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al., · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning,
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, · 2020
Cited alongside, same era.
S. Liu, S. Wang, P. Zhang, K. Xu, X. Liu, C. Zhang, F. Gao, · 2022
Later among the works it cites.
Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences,
S. Wang, X. Liu, S. Liu, J. Jin, W. Tu, X. Zhu, E. Zhu, · 2022
Later among the works it cites.
Simplemkkm: Simple multiple kernel k-means,
X. Liu, · 2022
Later among the works it cites.
Multi-level feature learning for contrastive multi-view clustering,
J. Xu, H. Tang, Y. Ren, L. Peng, X. Zhu, L. He, · 2022
Later among the works it cites.
Stationary diffusion state neural estimation for multiview clustering,
C. Liu, Z. Liao, Y. Ma, K. Zhan, · 2022
Later among the works it cites.
Self-supervised discriminative feature learning for deep multi-view clustering,
J. Xu, Y. Ren, H. Tang, Z. Yang, L. Pan, Y. Yang, X. Pu, S. Y. Philip, L. He, · 2022
Later among the works it cites.
Self-supervised discriminative feature learning for deep multi-view clustering,
J. Xu, Y. Ren, H. Tang, Z. Yang, L. Pan, Y. Yang, X. Pu, S. Y. Philip, L. He, · 2022
Later among the works it cites.
Scalable multi-view clustering with graph filtering,
L. Liu, P. Chen, G. Luo, Z. Kang, Y. Luo, S. Han, · 2022
Later among the works it cites.
Mixed graph contrastive network for semi-supervised node classification,
X. Yang, Y. Liu, S. Zhou, X. Liu, E. Zhu, · 2022
Later among the works it cites.
Interpolation-based contrastive learning for few-label semi-supervised learning,
X. Yang, X. Hu, S. Zhou, X. Liu, E. Zhu, · 2022
Later among the works it cites.
Deep graph clustering via dual correlation reduction,
Y. Liu, W. Tu, S. Zhou, X. Liu, L. Song, X. Yang, E. Zhu, · 2022
Later among the works it cites.
Hard sample aware network for contrastive deep graph clustering,
Y. Liu, X. Yang, S. Zhou, X. Liu, Z. Wang, K. Liang, W. Tu, L. Li, J. Duan, C. Chen, · 2022
Later among the works it cites.
Contrastive deep graph clustering with learnable augmentation,
X. Yang, Y. Liu, S. Zhou, S. Wang, X. Liu, E. Zhu, · 2022
Later among the works it cites.
Deep safe multi-view clustering: Reducing the risk of clustering performance degradation caused by view increase,
H. Tang, Y. Liu, · 2022
Later among the works it cites.
Dealmvc: Dual contrastive calibration for multi-view clustering,
X. Yang, J. Jin, S. Wang, K. Liang, Y. Liu, Y. Wen, S. Liu, S. Zhou, X. Liu, E. Zhu, · 2023
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Simple contrastive graph clustering,
Y. Liu, X. Yang, S. Zhou, X. Liu, S. Wang, K. Liang, W. Tu, L. Li, · 2023
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Cluster-guided contrastive graph clustering network,
X. Yang, Y. Liu, S. Zhou, S. Wang, W. Tu, Q. Zheng, X. Liu, L. Fang, E. Zhu, · 2023
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Convert: Contrastive graph clustering with reliable augmentation,
X. Yang, C. Tan, Y. Liu, K. Liang, S. Wang, S. Zhou, J. Xia, S. Z. Li, X. Liu, E. Zhu, · 2023
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