Fetching the paper…
Reading the bibliography…
Deep clustering successfully provides more effective features than conventional ones and thus becomes an important technique in current unsupervised learning.
J. MacQueen et al. , “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , vol. 1, no. 14. Oakland, CA, USA, 1967, pp. 281–297
1967
Earlier work this paper cites.
K. C. Gowda and G. Krishna, “Agglomerative clustering using the concept of mutual nearest neighbourhood,” Pattern Recogn. , vol. 10, no. 2, pp. 105–112, 1978
1978
Earlier work this paper cites.
S. Lloyd, “Least squares quantization in pcm,” IEEE transactions on information theory , vol. 28, no. 2, pp. 129–137, 1982
1982
Earlier work this paper cites.
L. Zelnik-Manor and P. Perona, “Self-tuning spectral clustering,” in NeurIPS , 2005, pp. 1601–1608
2005
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in CVPR , vol. 2. IEEE, 2006, pp. 1735–1742
2006
Earlier work this paper cites.
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in NeurIPS , 2007, pp. 153–160
2007
Earlier work this paper cites.
Y. Bengio and J.-S. Senécal, “Adaptive importance sampling to accelerate training of a neural probabilistic language model,” IEEE Trans. Neural Netw. , vol. 19, no. 4, pp. 713–722, 2008
2008
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
D. Cai, X. He, X. Wang, H. Bao, and J. Han, “Locality preserving nonnegative matrix factorization.” in IJCAI , vol. 9, 2009, pp. 1010–1015
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by convex risk minimization,” IEEE Trans. Inf. Theory , vol. 56, no. 11, pp. 5847–5861, 2010
2010
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in AISTATS , 2010, pp. 297–304
2010
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of Machine Learning Research , vol. 11, no. 12, 2010
2010
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in CVPR . IEEE, 2010, pp. 2528–2535
2010
Earlier work this paper cites.
A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in AISTATS , 2011, pp. 215–223
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Cited alongside, same era.
Y. Le and X. Yang, “Tiny imagenet visual recognition challenge,” CS 231N , vol. 7, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in ICML , 2016, pp. 478–487
2016
Cited alongside, same era.
J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in CVPR , 2016, pp. 5147–5156
2018
Later among the works it cites.
K. Wei, M. Yang, H. Wang, C. Deng, and X. Liu, “Adversarial fine-grained composition learning for unseen attribute-object recognition,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 3741–3749
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Wu, K. Long, F. Wang, C. Qian, C. Li, Z. Lin, and H. Zha, “Deep comprehensive correlation mining for image clustering,” in ICCV , 2019, pp. 8150–8159
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in NeurIPS , 2016, pp. 2172–2180
2016
Cited alongside, same era.
J. Chang, L. Wang, G. Meng, S. Xiang, and C. Pan, “Deep adaptive image clustering,” in ICCV , 2017, pp. 5879–5887
2017
Cited alongside, same era.
W. Hu, T. Miyato, S. Tokui, E. Matsumoto, and M. Sugiyama, “Learning discrete representations via information maximizing self-augmented training,” in International Conference on Machine Learning , 2017, pp. 1558–1567
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout, and D. Cremers, “Associative deep clustering: Training a classification network with no labels,” in GCPR . Springer, 2018, pp. 18–32
2018
Cited alongside, same era.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in ECCV , 2018, pp. 132–149
2018
Cited alongside, same era.
X. Ji, J. F. Henriques, and A. Vedaldi, “Invariant information clustering for unsupervised image classification and segmentation,” in ICCV , 2019, pp. 9865–9874
2019
Later among the works it cites.
X. Yang, C. Deng, F. Zheng, J. Yan, and W. Liu, “Deep spectral clustering using dual autoencoder network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4066–4075
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Ahn, S. X. Hu, A. Damianou, N. D. Lawrence, and Z. Dai, “Variational information distillation for knowledge transfer,” in CVPR , 2019, pp. 9163–9171
2019
Later among the works it cites.
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic, “On mutual information maximization for representation learning,” in ICLR , 2019
2019
Later among the works it cites.
J. Huang, S. Gong, and X. Zhu, “Deep semantic clustering by partition confidence maximisation,” in CVPR , 2020, pp. 8849–8858
2020
Later among the works it cites.
X. Yang, C. Deng, K. Wei, J. Yan, and W. Liu, “Adversarial learning for robust deep clustering,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
Z. Dang, C. Deng, X. Yang, and H. Huang, “Multi-scale fusion subspace clustering using similarity constraint,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6658–6667
2020
Later among the works it cites.
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR , 2020, pp. 9729–9738
2020
Later among the works it cites.
2020
Later among the works it cites.
B. Gholami, P. Sahu, O. Rudovic, K. Bousmalis, and V. Pavlovic, “Unsupervised multi-target domain adaptation: An information theoretic approach,” IEEE Trans. Image Process. , vol. 29, pp. 3993–4002, 2020
2020
Later among the works it cites.