A sober look at the unsupervised learning of disentangled representations and their evaluation

被引:0
|
作者
Locatello, Francesco [1 ]
Bauer, Stefan [2 ]
Lucic, Mario [3 ]
Rätsch, Gunnar [1 ]
Gelly, Sylvain [3 ]
Schölkopf, Bernhard [2 ]
Bachem, Olivier [4 ]
机构
[1] Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich,8092, Switzerland
[2] Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, Tübingen,72076, Germany
[3] Google Research, Brain Team, Brandschenkestrasse 110, Zürich,8002, Switzerland
[4] Google Research, Brain Team, Brandschenkestrasse 110, Zürich,8002, Switzerland
关键词
80;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
  • [1] A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
    Locatello, Francesco
    Bauer, Stefan
    Lucic, Mario
    Ratsch, Gunnar
    Gelly, Sylvain
    Schoelkopf, Bernhard
    Bachem, Olivier
    JOURNAL OF MACHINE LEARNING RESEARCH, 2020, 21
  • [2] A Commentary on the Unsupervised Learning of Disentangled Representations
    Locatello, Francesco
    Bauer, Stefan
    Lucie, Mario
    Raetsch, Gunnar
    Gelly, Sylvain
    Schoelkopf, Bernhard
    Bachem, Olivier
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 13681 - 13684
  • [3] Unsupervised Learning of Disentangled Representations from Video
    Denton, Emily
    Birodkar, Vighnesh
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [4] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
    Locatello, Francesco
    Bauer, Stefan
    Lucic, Mario
    Ratsch, Gunnar
    Gelly, Sylvain
    Scholkopf, Bernhard
    Bachem, Olivier
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
  • [5] Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
    Stuhmer, Jan
    Turner, Richard E.
    Nowozin, Sebastian
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, 2020, 108
  • [6] Geometric Inductive Biases for Identifiable Unsupervised Learning of Disentangled Representations
    Pan, Ziqi
    Niu, Li
    Zhang, Liqing
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 8, 2023, : 9372 - 9380
  • [7] Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
    Hsu, Wei-Ning
    Zhang, Yu
    Glass, James
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [8] Linear Disentangled Representations and Unsupervised Action Estimation
    Painter, Matthew
    Hare, Jonathon
    Prugel-Bennett, Adam
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [9] Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory
    Zuo, Lianrui
    Dewey, Blake E.
    Liu, Yihao
    He, Yufan
    Newsome, Scott D.
    Mowry, Ellen M.
    Resnick, Susan M.
    Prince, Jerry L.
    Carass, Aaron
    NEUROIMAGE, 2021, 243
  • [10] Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
    Tonin, Francesco
    Patrinos, Panagiotis
    Suykens, Johan A. K.
    NEURAL NETWORKS, 2021, 142 : 661 - 679