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Clustering with Deep Learning: Taxonomy and New Methods
v1v2 (latest)

Clustering with Deep Learning: Taxonomy and New Methods

23 January 2018
Elie Aljalbout
Vladimir Golkov
Yawar Siddiqui
Maximilian Strobel
Zorah Lähner
ArXiv (abs)PDFHTML

Papers citing "Clustering with Deep Learning: Taxonomy and New Methods"

28 / 78 papers shown
MorphoCluster: Efficient Annotation of Plankton images by Clustering
MorphoCluster: Efficient Annotation of Plankton images by ClusteringItalian National Conference on Sensors (INS), 2020
Simon-Martin Schroder
R. Kiko
Reinhard Koch
291
50
0
04 May 2020
Learning to Structure an Image with Few Colors
Learning to Structure an Image with Few ColorsComputer Vision and Pattern Recognition (CVPR), 2020
Yunzhong Hou
Liang Zheng
Stephen Gould
MQ
258
22
0
17 Mar 2020
Deep Inverse Feature Learning: A Representation Learning of Error
Deep Inverse Feature Learning: A Representation Learning of Error
B. Ghazanfari
Fatemeh Afghah
99
3
0
09 Mar 2020
Memory-Based Graph Networks
Memory-Based Graph NetworksInternational Conference on Learning Representations (ICLR), 2020
Amir Hosein Khas Ahmadi
Kaveh Hassani
Parsa Moradi
Leo Lee
Q. Morris
GNN
326
99
0
21 Feb 2020
Set2Graph: Learning Graphs From Sets
Set2Graph: Learning Graphs From SetsNeural Information Processing Systems (NeurIPS), 2020
Hadar Serviansky
Nimrod Segol
Jonathan Shlomi
Kyle Cranmer
Eilam Gross
Haggai Maron
Y. Lipman
PINNGNN
387
36
0
20 Feb 2020
Signal Clustering with Class-independent Segmentation
Signal Clustering with Class-independent SegmentationIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019
Stefano Gasperini
Magdalini Paschali
Carsten Hopke
David Wittmann
Nassir Navab
104
12
0
18 Nov 2019
Centroid Based Concept Learning for RGB-D Indoor Scene Classification
Centroid Based Concept Learning for RGB-D Indoor Scene Classification
Ali Ayub
Alan R. Wagner
144
0
0
01 Nov 2019
Meta-Learning to Cluster
Meta-Learning to Cluster
Yibo Jiang
Nakul Verma
FedML
110
7
0
30 Oct 2019
Speaker diarization using latent space clustering in generative
  adversarial network
Speaker diarization using latent space clustering in generative adversarial networkIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019
Monisankha Pal
Manoj Kumar
Raghuveer Peri
Tae Jin Park
So Hyun Kim
C. Lord
Somer Bishop
Shrikanth Narayanan
124
20
0
24 Oct 2019
Unsupervised Multi-Task Feature Learning on Point Clouds
Unsupervised Multi-Task Feature Learning on Point CloudsIEEE International Conference on Computer Vision (ICCV), 2019
Kaveh Hassani
Mike Haley
SSL3DPC
277
201
0
18 Oct 2019
Mixture-of-Experts Variational Autoencoder for Clustering and Generating
  from Similarity-Based Representations on Single Cell Data
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell DataInternational Conference on Learning Representations (ICLR), 2019
Andreas Kopf
Vincent Fortuin
Vignesh Ram Somnath
Manfred Claassen
DRL
161
12
0
17 Oct 2019
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Laura Manduchi
Matthias Huser
Julia E. Vogt
Gunnar Rätsch
Vincent Fortuin
AI4TS
133
2
0
03 Oct 2019
Deep Amortized Clustering
Deep Amortized Clustering
Juho Lee
Yoonho Lee
Yee Whye Teh
FedML
136
23
0
30 Sep 2019
Quantization-Based Regularization for Autoencoders
Quantization-Based Regularization for Autoencoders
Hanwei Wu
M. Flierl
DRL
166
2
0
27 May 2019
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
Loic Landrieu
Mohamed Boussaha
3DPC
156
160
0
03 Apr 2019
Label-Removed Generative Adversarial Networks Incorporating with K-Means
Label-Removed Generative Adversarial Networks Incorporating with K-Means
Ce Wang
Zhangling Chen
Kun Shang
GAN
139
25
0
19 Feb 2019
Neural Clustering Processes
Neural Clustering Processes
Ari Pakman
Yueqi Wang
Catalin Mitelut
JinHyung Lee
Liam Paninski
BDL
280
5
0
28 Dec 2018
Deep Clustering Based on a Mixture of Autoencoders
Deep Clustering Based on a Mixture of Autoencoders
Shlomo E. Chazan
Sharon Gannot
Jacob Goldberger
120
39
0
16 Dec 2018
Survey of state-of-the-art mixed data clustering algorithms
Survey of state-of-the-art mixed data clustering algorithms
Amir Ahmad
Shehroz S. Khan
279
192
0
11 Nov 2018
Deep clustering: On the link between discriminative models and K-means
Deep clustering: On the link between discriminative models and K-means
Mohammed Jabi
M. Pedersoli
A. Mitiche
Ismail Ben Ayed
197
117
0
09 Oct 2018
Sparse Label Smoothing Regularization for Person Re-Identification
Sparse Label Smoothing Regularization for Person Re-Identification
Jean-Paul Ainam
Ke Qin
Guisong Liu
Guangchun Luo
149
0
0
13 Sep 2018
Semantically Meaningful View Selection
Semantically Meaningful View Selection
Joris Guérin
O. Gibaru
E. Nyiri
Stéphane Thiery
Byron Boots
148
7
0
26 Jul 2018
Superpixel Sampling Networks
Superpixel Sampling Networks
Varun Jampani
Deqing Sun
Ming-Yuan Liu
Ming-Hsuan Yang
Jan Kautz
SSeg
139
261
0
26 Jul 2018
Improving Image Clustering With Multiple Pretrained CNN Feature
  Extractors
Improving Image Clustering With Multiple Pretrained CNN Feature ExtractorsBritish Machine Vision Conference (BMVC), 2018
Joris Guérin
Byron Boots
128
35
0
20 Jul 2018
Learning Neural Models for End-to-End Clustering
Learning Neural Models for End-to-End Clustering
B. Meier
Ismail Elezi
Mohammadreza Amirian
Oliver Durr
Thilo Stadelmann
SSL
142
17
0
11 Jul 2018
Deep $k$-Means: Jointly clustering with $k$-Means and learning
  representations
Deep kkk-Means: Jointly clustering with kkk-Means and learning representationsPattern Recognition Letters (PR), 2018
Maziar Moradi Fard
Thibaut Thonet
Éric Gaussier
218
262
0
26 Jun 2018
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Vincent Fortuin
Matthias Huser
Francesco Locatello
Heiko Strathmann
Gunnar Rätsch
BDLAI4TS
333
151
0
06 Jun 2018
Convolutional Embedded Networks for Population Scale Clustering and
  Bio-ancestry Inferencing
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing
Md. Rezaul Karim
Michael Cochez
Achille Zappa
Ratnesh Sahay
Oya Beyan
Dietrich-Rebholz Schuhmann
Stefan Decker
95
0
0
30 May 2018
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