Disentangled Representation Learning

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Overview

Disentangled representation learning studies how to encode separate factors—such as content and style, identity and viewpoint, or disease and acquisition conditions—in distinct parts of a representation. Common approaches include variational autoencoders, contrastive objectives, sparse or orthogonal subspaces, causal models, and symmetry-based methods; many applications use the resulting factors for editing, robust prediction, or transfer across domains. Work now spans images, speech, graphs, time series, medicine, and robotics, with increasing attention to interventions and practical controllability. A central tension remains: factors are generally not identifiable from unlabelled data alone without assumptions or useful supervision, and standard disentanglement metrics can fail when their assumptions do not hold.

Latest papers

CardsList

234 papers24 papers in the last four weeks, up 118% on the four weeks before. 0.1% of all new papers.

  1. DipGuava: Disentangling Personalized Gaussian Features for 3D Head Avatars from Monocular Video

    Mar 30, 2026Jeonghaeng Lee, Seok Keun Choi, Zhixuan Li +23D Avatar Generation3D Gaussian Splatting

    Yonsei University, Korea · Nanyang Technological University, Singapore

  2. MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement

    Mar 29, 2026Yejia Liu, Hengle Jiang, Haoxian Liu +23D Human Pose EstimationDisentangled Representation Learning

    Department of Computer Science and Engineering, Hong Kong University of Science and Technology

  3. A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection

    Mar 26, 2026SuYeon Kim, Wongyu Lee, MyeongAh ChoPoint Cloud Anomaly DetectionUnsupervised Anomaly Detection

    Kyung Hee University, Republic of Korea

  4. CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

    Mar 25, 2026Bowen Lu, Lianqiang Yang, Teng Li +1OOD GeneralizationGraph Neural Networks

    Anhui University, Hefei, China · Carnegie Mellon University, Pittsburgh, Commonwealth of Pennsylvania, USA

  5. Causality-Driven Disentangled Representation Learning in Multiplex Graphs

    Mar 25, 2026Saba Nasiri, Selin Aviyente, Dorina ThanouGraph Representation LearningDisentangled Representation Learning

    EPFL, Lausanne, Switzerland · Department of Electrical and Computer Engineering at Michigan State University, East Lansing, MI

  6. SelfTTS: cross-speaker style transfer through explicit embedding disentanglement and self-refinement using self-augmentation

    Mar 23, 2026Lucas H. Ueda, João G. T. Lima, Pedro R. Corrêa +3Disentangled Representation LearningTTS Synthesis

    Universidade Estadual de Campinas (UNICAMP), Brazil · CPQD, Brazil

  7. Evaluating Factor-Wise Auxiliary Dynamics Supervision for Latent Structure and Robustness in Simulated Humanoid Locomotion

    Mar 22, 2026Chayanin ChamachotLatent Dynamics ModelingDisentangled Representation Learning

    Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

  8. Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation

    Mar 17, 2026Chenggong Hu, Yi Wang, Mengqi Xue +3Image GenerationLatent Diffusion Models

    Zhejiang University · Hangzhou City University

  9. Self-supervised Disentanglement of Disease Effects from Aging in 3D Medical Shapes

    Mar 16, 2026Jakaria Rabbi, Nilanjan Ray, Dana CobzasRepresentation DisentanglementDisentangled Representation Learning

    Department of Computing Science, University of Alberta, Edmonton, Canada · Department of Computer Science, MacEwan University, Edmonton, Canada

  10. U-Face: An Efficient and Generalizable Framework for Unsupervised Facial Attribute Editing via Subspace Learning

    Mar 14, 2026Bo Liu, Xuan Cui, Run Zeng +5Image EditingDisentangled Representation Learning

    Chongqing Key Laboratory of Intelligent Sensing and Blockchain Technology, Chongqing Technology and Business University, Chongqing, China · School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China · School of Software, Northwestern Polytechnical University, Xi’an, China

  11. SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification

    Mar 13, 2026Furui Chen, Han Wang, Yuhan Sun +5Cross-Modal Representation LearningCross-Modal Retrieval

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · University of Chinese Academy of Sciences, Beijing 100049, China +1

  12. Statistical and structural identifiability in representation learning

    Mar 12, 2026Walter Nelson, Marco Fumero, Theofanis Karaletsos +1Representation IdentifiabilityDisentangled Representation Learning

    Institute of Science and Technology Austria · Chan Zuckerberg Initiative

  13. Disentangled Representation Learning through Unsupervised Symmetry Group Discovery

    Mar 12, 2026Barthélémy Dang-Nhu, Louis Annabi, Sylvain ArgentieriDisentangled Representation LearningUnsupervised Representation Learning

    Sorbonne Universit´e, CNRS, Institut des Syst`emes Intelligents et de Robotique, ISIR, F-75005 Paris, France

  14. CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement

    Mar 12, 2026Alex Gn, Fan Li, S Kuniyilh +1Privacy-Preserving Representation LearningHuman Activity Recognition

  15. A Unified Latent Space Disentanglement VAE Framework with Robust Disentanglement Effectiveness Evaluation

    Mar 11, 2026Xiaoan Lang, Md Mostafizer Rahman, Fang LiuVariational AutoencodersDisentangled Representation Learning

    Department of Applied and Computational Mathematics and Statistics University of Notre Dame, Notre Dame, IN, USA · Lucy Family Institute for Data & Society University of Notre Dame, Notre Dame, IN, USA

  16. Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck

    Mar 11, 2026Hongbin Zhang, Kehai Chen, Xuefen Bai +4Bias in LLM-as-a-JudgeLinguistic Bias in Language Models

    Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · Keeta AI, Meituan, Beijing, China

  17. Koopman Regularized Deep Speech Disentanglement for Speaker Verification

    Mar 5, 2026Nikos Chazaridis, Mohammad Belal, Rafael Mestre +2Speaker VerificationSpeaker Disentanglement

    School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, U.K. · Marine Geosciences, National Oceanography Centre, SO14 3ZH Southampton, U.K.

  18. Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning

    Mar 5, 2026Rui Zhao, Bin Shi, Kai Sun +1Weakly Supervised LearningDisentangled Representation Learning

    School of Computer Science and Technology, Xi’an Jiaotong University · Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University · School of Distance Education, Xi’an Jiaotong University

  19. Nearest-Neighbor Density Estimation for Dependency Suppression

    Mar 4, 2026Kathleen Anderson, Thomas MartinetzPrivacy-Preserving MLUnsupervised Representation Learning

    Institute for Neuro- and Bioinformatics, University of Lübeck, Germany

  20. Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features

    Mar 3, 2026Kyle Janse van Rensburg, Benjamin van Niekerk, Herman KamperSelf-Supervised Speech Representation LearningAudio Representation Learning

    Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa · Concordia University, Canada

  21. Teacher-Guided Causal Interventions for Image Denoising: Orthogonal Content-Noise Disentanglement in Vision Transformers

    Mar 1, 2026Kuai Jiang, Zhaoyan Ding, Guijuan Zhang +2Disentangled Representation LearningCausal Representation Learning

    China University of Mining and Technology · Shandong Normal University · Qilu University of Technology

  22. Decoupling Motion and Geometry in 4D Gaussian Splatting

    Mar 1, 2026Yi Zhang, Yulei Kang, Jiangxin Sun +34D ReconstructionDynamic Scene Reconstruction

    Sun Yat-sen University · University of Trento · Huazhong University of Science and Technology +1

  23. Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations

    Feb 27, 2026Shruti Joshi, Théo Saulus, Wieland Brendel +3Representation IdentifiabilityDisentangled Representation Learning

    Mila - Québec AI Institute & Université de Montréal · Max-Planck-Institute for Intelligent Systems, ELLIS Institute Tübingen, University of Tübingen

  24. Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning

    Feb 27, 2026Kohei Obata, Taichi Murayama, Zheng Chen +2Time Series Representation LearningDisentangled Representation Learning

    SANKEN, Osaka University, Osaka, Japan · Yokohama National University, Kanagawa, Japan

  25. Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models

    Feb 26, 2026Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré +4Disentangled Representation LearningPhysics-Informed Generative Modeling

    Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439 · High Energy Physics Division, Argonne National Laboratory, Lemont, IL 60439, USA · Computational Science Division, Argonne National Laboratory, Lemont, IL 60439 +1

  26. SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis

    Feb 26, 2026Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3Disentangled Representation LearningMultimodal Disentangled Representation Learning

    Department of Mechanical Engineering, University of Washington, Seattle, WA, USA · Department of Radiology, University of Washington, Seattle, WA, USA

  27. Disentangling Shared and Target-Enriched Topics via Background-Contrastive Non-negative Matrix Factorization

    Feb 25, 2026Yixuan Li, Archer Y. Yang, Yue LiNon-Negative Matrix FactorizationDisentangled Representation Learning

    Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada · Mila – Quebec AI Institute, Montreal, QC, Canada · School of Computer Science, McGill University, Montreal, QC, Canada

  28. Disentangled Hierarchical VAE for 3D Human-Human Interaction Generation

    Feb 24, 2026Zichen Geng, Zeeshan Hayder, Bo Miao +3Human Motion GenerationVariational Autoencoders

    Department of CSSE, The University of Western Australia, Crawley WA 6009, Australia · Data61, CSIRO, Acton ACT 2601, Australia · Australian Institute for Machine Learning, The University of Adelaide, Adelaide SA 5000, Australia +1

  29. Controlled Face Manipulation and Synthesis for Data Augmentation

    Feb 22, 2026Joris Kirchner, Amogh Gudi, Marian Bittner +1Synthetic Data AugmentationImage Editing

    Vicarious Perception Technologies (VicarVision) · Delft University of Technology

  30. Automated Disentangling Analysis of Skin Colour for Lesion Images

    Feb 22, 2026Wenbo Yang, Eman Rezk, Walaa M. Moursi +1Disentangled Representation LearningRepresentation Disentanglement

    University of Waterloo, Waterloo, ON, Canada, N2L 3G1