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Invariant Information Clustering for Unsupervised Image Classification
  and Segmentation
v1v2v3v4 (latest)

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

IEEE International Conference on Computer Vision (ICCV), 2018
17 July 2018
Xu Ji
João F. Henriques
Andrea Vedaldi
    SSLVLM
ArXiv (abs)PDFHTMLGithub (881★)

Papers citing "Invariant Information Clustering for Unsupervised Image Classification and Segmentation"

14 / 514 papers shown
Title
Adversarial Deep Embedded Clustering: on a better trade-off between
  Feature Randomness and Feature Drift
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature DriftIEEE Transactions on Knowledge and Data Engineering (TKDE), 2019
Nairouz Mrabah
Mohamed Bouguessa
Riadh Ksantini
OOD
98
57
0
26 Sep 2019
Unsupervised Deep Feature Transfer for Low Resolution Image
  Classification
Unsupervised Deep Feature Transfer for Low Resolution Image Classification
Yuanwei Wu
Ziming Zhang
Guanghui Wang
275
23
0
27 Aug 2019
Learning Deep Representations by Mutual Information for Person
  Re-identification
Learning Deep Representations by Mutual Information for Person Re-identification
Peng Chen
Tong Jia
Pengfei Wu
Jianjun Wu
Dongyue Chen
SSL
156
4
0
16 Aug 2019
On Mutual Information Maximization for Representation Learning
On Mutual Information Maximization for Representation LearningInternational Conference on Learning Representations (ICLR), 2019
Michael Tschannen
Josip Djolonga
Paul Kishan Rubenstein
Sylvain Gelly
Mario Lucic
SSL
401
534
0
31 Jul 2019
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge
  Distillation
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge DistillationInternational Conference on Machine Learning and Applications (ICMLA), 2019
Jayaraman J. Thiagarajan
Satyananda Kashyap
Alexandros Karargyris
VLM
89
10
0
26 Jul 2019
Unsupervised Learning of Object Keypoints for Perception and Control
Unsupervised Learning of Object Keypoints for Perception and ControlNeural Information Processing Systems (NeurIPS), 2019
Tejas D. Kulkarni
Ankush Gupta
Catalin Ionescu
Sebastian Borgeaud
Malcolm Reynolds
Andrew Zisserman
Volodymyr Mnih
SSLOCL
196
209
0
19 Jun 2019
Contrastive Multiview Coding
Contrastive Multiview CodingEuropean Conference on Computer Vision (ECCV), 2019
Yonglong Tian
Dilip Krishnan
Phillip Isola
SSL
623
2,597
0
13 Jun 2019
Deep Semi-Supervised Anomaly Detection
Deep Semi-Supervised Anomaly DetectionInternational Conference on Learning Representations (ICLR), 2019
Lukas Ruff
Robert A. Vandermeulen
Nico Görnitz
Alexander Binder
Emmanuel Müller
K. Müller
Matthias Kirchler
UQCV
259
634
0
06 Jun 2019
Learning Representations by Maximizing Mutual Information Across Views
Learning Representations by Maximizing Mutual Information Across ViewsNeural Information Processing Systems (NeurIPS), 2019
Philip Bachman
R. Devon Hjelm
William Buchwalter
SSL
437
1,587
0
03 Jun 2019
Emergence of Object Segmentation in Perturbed Generative Models
Emergence of Object Segmentation in Perturbed Generative ModelsNeural Information Processing Systems (NeurIPS), 2019
Adam Bielski
Paolo Favaro
264
103
0
29 May 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised LearningNeural Information Processing Systems (NeurIPS), 2019
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
478
3,324
0
06 May 2019
SEIGAN: Towards Compositional Image Generation by Simultaneously
  Learning to Segment, Enhance, and Inpaint
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint
Pavel Ostyakov
Roman Suvorov
Elizaveta Logacheva
Oleg Khomenko
Sergey I. Nikolenko
GAN
169
24
0
19 Nov 2018
XAI Beyond Classification: Interpretable Neural Clustering
XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng
Yunfan Li
Ivor W. Tsang
Erik Cambria
Jiancheng Lv
Qiufeng Wang
166
83
0
22 Aug 2018
Learning deep representations by mutual information estimation and
  maximization
Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm
A. Fedorov
Samuel Lavoie-Marchildon
Karan Grewal
Phil Bachman
Adam Trischler
Yoshua Bengio
SSLDRL
789
2,877
0
20 Aug 2018
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