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Deep $k$-Means: Jointly clustering with $k$-Means and learning
  representations
v1v2 (latest)

Deep kkk-Means: Jointly clustering with kkk-Means and learning representations

26 June 2018
Maziar Moradi Fard
Thibaut Thonet
Éric Gaussier
ArXiv (abs)PDFHTML

Papers citing "Deep $k$-Means: Jointly clustering with $k$-Means and learning representations"

15 / 15 papers shown
Title
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
568
10,591
0
17 Feb 2020
Clustering with Deep Learning: Taxonomy and New Methods
Clustering with Deep Learning: Taxonomy and New Methods
Elie Aljalbout
Vladimir Golkov
Yawar Siddiqui
Maximilian Strobel
Daniel Cremers
78
242
0
23 Jan 2018
Deep Subspace Clustering Networks
Deep Subspace Clustering Networks
Pan Ji
Tong Zhang
Hongdong Li
Mathieu Salzmann
Ian Reid
SSL
69
512
0
08 Sep 2017
CNN-Based Joint Clustering and Representation Learning with Feature
  Drift Compensation for Large-Scale Image Data
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data
Chih-Chung Hsu
Chia-Wen Lin
SSL
118
128
0
19 May 2017
Deep Clustering via Joint Convolutional Autoencoder Embedding and
  Relative Entropy Minimization
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Kamran Ghasedi Dizaji
Amirhossein Herandi
Cheng Deng
Weidong (Tom) Cai
Heng-Chiao Huang
49
524
0
20 Apr 2017
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible
  Representations
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
E. Agustsson
Fabian Mentzer
Michael Tschannen
Lukas Cavigelli
Radu Timofte
Luca Benini
Luc Van Gool
MQ
97
483
0
03 Apr 2017
Learning Discrete Representations via Information Maximizing
  Self-Augmented Training
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu
Takeru Miyato
Seiya Tokui
Eiichi Matsumoto
Masashi Sugiyama
94
452
0
28 Feb 2017
Variational Deep Embedding: An Unsupervised and Generative Approach to
  Clustering
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Zhuxi Jiang
Yin Zheng
Huachun Tan
Bangsheng Tang
Hanning Zhou
BDLDRL
101
733
0
16 Nov 2016
Deep Unsupervised Clustering with Gaussian Mixture Variational
  Autoencoders
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul
P. Mediano
M. Garnelo
M. J. Lee
Hugh Salimbeni
Kai Arulkumaran
Murray Shanahan
DRL
84
658
0
08 Nov 2016
Categorical Reparameterization with Gumbel-Softmax
Categorical Reparameterization with Gumbel-Softmax
Eric Jang
S. Gu
Ben Poole
BDL
371
5,391
0
03 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random
  Variables
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison
A. Mnih
Yee Whye Teh
BDL
207
2,541
0
02 Nov 2016
Towards K-means-friendly Spaces: Simultaneous Deep Learning and
  Clustering
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Bo Yang
Xiao Fu
N. Sidiropoulos
Mingyi Hong
DRL
92
878
0
15 Oct 2016
Joint Unsupervised Learning of Deep Representations and Image Clusters
Joint Unsupervised Learning of Deep Representations and Image Clusters
Jianwei Yang
Devi Parikh
Dhruv Batra
SSL
69
819
0
13 Apr 2016
Unsupervised Deep Embedding for Clustering Analysis
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie
Ross B. Girshick
Ali Farhadi
SSL
115
2,889
0
19 Nov 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.2K
150,472
0
22 Dec 2014
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