Disentangled Representation Learning

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Disentanglement with Holographic Reduced Representations
Jun 8, 2026Jhonny J. Velasquez Olivera, Christo K. Thomas, Walid SaadDisentangled Representation Learning Unsupervised Representation Learning
Virginia Tech · Worcester Polytechnic Institute
Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
Jun 7, 2026Abdul Moeed, Stefan Schrod, Martin Rohbeck +4Spatial Transcriptomics Disentangled Representation Learning
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany · Helmholtz Information & Data Science School for Health, Germany · Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany +4
HybridCodec: Fast Dual-Stream, Semantically Enhanced Neural Audio Codec
Jun 4, 2026Arjun Gangwar, S UmeshDisentangled Representation Learning Audio Representation Learning
Indian Institute of Technology, Madras, India
Diff-CA: Separating Common and Salient Factors with Diffusion Models
Jun 4, 2026Michaël Soumm, Alexandre Fournier Montgieux, Yunlong He +2Disentangled Representation Learning Diffusion-Based Image Editing
INRIA at Univ. Grenoble Alpes · CEA List, Palaiseau · Télécom Paris, Institut Polytechnique de Paris
Multi-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition
Jun 4, 2026Seung Hwan Cho, Young-Min KimDisentangled Representation Learning Multi-Task Learning
Department of Industrial Data Engineering, Hanyang University, Seoul, South Korea · School of Interdisciplinary Industrial Studies, Hanyang University, Seoul, South Korea
CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors
Jun 4, 2026Shengxi Li, Zhaokun Hu, Ce Zheng +3Disentangled Representation Learning Conditional Image Generation
Department of Electronic Information Engineering, Beihang University, Beijing 100191, China · School of Cyber Science and Technology, Beihang University, Beijing 100191, China · College of Electronic Science, National University of Defense Technology, Hunan 410073, China +1
RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
Jun 3, 2026Vasiliki Rizou, Pascal Frossard, Dorina ThanouDisentangled Representation Learning Cross-Modal Representation Learning
EPFL
Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
Jun 3, 2026Chuankai Xu, Cristiane De Carvalho Singulane, Mohammad Abuannadi +10Disentangled Representation Learning Causal Representation Learning
University of Virginia, Charlottesville, VA, USA · University of Chicago, Chicago, IL, USA · Ohio State University, Columbus, OH, USA +5
ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
Jun 2, 2026Yikang Gui, Bikramjit Banerjee, Prashant DoshiDisentangled Representation Learning Reinforcement Learning
School of Computing, University of Georgia · School of Computing Sciences & Computer Engineering, The University of Southern Mississippi
CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
Jun 1, 2026Chengfeng Wu, Tao Zou, Yanru Wu +1Disentangled Representation Learning Causal Representation Learning
Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing, China
Disentanglement-Based Equivariant Learning for Compositional VQA
Jun 1, 2026Zhou Du, Zhaoquan Yuan, Xiao Wu +1Visual Question Answering Disentangled Representation Learning
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611730, China, and also with Engineering Research Center of Sustainable Urban Intelligence Transportation, Ministry of Education, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China, and also with Peng Cheng Laboratory, ShenZhen 518055, China
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Jun 1, 2026Canyixing Cui, Tao Wu, Xingping Xian +3Disentangled Representation Learning Graph Neural Networks
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, China · Computational Communication Research Center, Beijing Normal University, Zhuhai, China +2
Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
May 29, 2026Rongzhen Zhao, Zhiyuan Li, Juho Kannala +1Disentangled Representation Learning Object-Centric Representation Learning
Department of Electrical Engineering and Automation, Aalto University, Finland · Department of Computer Science, Aalto University, Finland · Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
May 29, 2026Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer +2Disentangled Representation Learning Representation Disentanglement
Max Planck Institute for Informatics, SIC · Department of Computer Science, TU Darmstadt
Latent Space Disentanglement via Activation Steering for Interpretable Attribute Control in Symbolic Music Generation
May 29, 2026Ioannis Prokopiou, Pantelis Vikatos, Maximos Kaliakatsos-Papakostas +2Disentangled Representation Learning Controllable Music Generation
Athens University of Economics and Business Orfium Athens, Greece · Innovation Lab Orfium Athens, Greece · Department of Music Technology and Acoustics Hellenic Mediterranean University Rethymno, Greece +2
Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
May 29, 2026Nan Bao, Yifan Zhao, Wenzhuang Wang +1Disentangled Representation Learning Image Generation
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering and Qingdao Research Institute, Beihang University, China.
Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Disentangled Representation Learning Representation Learning
LIX, École Polytechnique IRT SystemX Palaiseau, France · IRT SystemX Palaiseau, France · Safran Tech Chateaufort, France
OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglement
May 29, 2026Bin Wang, Fadi DornaikaContrastive Learning Disentangled Representation Learning
University B.of theWang1Basqueand Country · IKERBASQUE,2
Text-guided Feature Disentanglement for Cross-modal Gait Recognition
May 29, 2026Zhiyang Lu, Ming ChengDisentangled Representation Learning Multimodal Robustness
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University. · Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China.
Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
May 29, 2026Honglin Xiong, Yuxian Tang, Feng Li +4Disentangled Representation Learning Magnetic Resonance Imaging
Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders
May 28, 2026Pierre-Antoine Lequeu, Camille Barboule, Benjamin PiwowarskiTransformer Interpretability Disentangled Representation Learning
Sorbonne Université, CNRS, ISIR, Paris, France · Orange Innovation
Bayesian Gated Non-Negative Contrastive Learning
May 27, 2026Peng Cui, Jiahao Zhang, Lijie HuContrastive Learning Disentangled Representation Learning
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
MERIT: Learning Disentangled Music Representations for Audio Similarity
May 26, 2026Abhinaba Roy, Junyi Liang, Dorien HerremansDisentangled Representation Learning Audio Representation Learning
Singapore University of Technology and Design
Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
May 26, 2026Qida Tan, Hongyu Yang, Wenchao DuContrastive Learning Gaze Estimation
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China · College of Computer Science, Sichuan University, Chengdu, China
Causal Representation Learning for Generalisable Recommendation
May 26, 2026Yorgos Felekis, Michael O'Riordan, Oriol Corcoll +1Disentangled Representation Learning Causal Representation Learning
University of Warwick · Spotify · Spotify & University College London
StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery
May 25, 2026Yuan-Hao WeiDisentangled Representation Learning Unsupervised Representation Learning
Adaptive Human-AI Coordination via Hierarchical Action Disentanglement
May 23, 2026Adnan Ahmad, Bahareh Nakisa, Mohammad Naim RastgooDisentangled Representation Learning Hierarchical RL
School of Information Technology Deakin University, Waurn Ponds, 3216, VIC, Australia · Faculty of Information Technology, Monash University, 3800, VIC, Australia
COSY: Compositional 3DGS Synthesis for Disentangled Human Head Editing
May 22, 2026Florian Barthel, Shalini De Mello, Koki Nagano +33D Gaussian Splatting Disentangled Representation Learning
Fraunhofer Heinrich-Hertz Institute, Germany · Humboldt University Berlin, Germany · NVIDIA, USA
Commutator-Induced Uncertainty in VAEs
May 22, 2026Tahereh Dehdarirad, Michael Felsberg, Gabriel Eilertsen +1Variational Autoencoders Disentangled Representation Learning
Computer Vision and Learning Systems (CVL), Linköping University, Sweden · Department of Science and Technology, Linköping University, Sweden
ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
May 21, 2026José Alberto Rodríguez, Luis Balderas, Miguel Lastra +2Variational Autoencoders Disentangled Representation Learning
Department of Computer Science and Artificial Intelligence DiCITS, iMUDS, DaSCI University of Granada, Granada, Spain 18071 · Advanced Medical Imaging Group, Instituto de Investigación Biosanitaria de Granada (ibs.Granada) · Department of Software Engineering DiCITS, iMUDS, DaSCI University of Granada, Granada, Spain 18071 +1

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Disentanglement with Holographic Reduced Representations

Jhonny J. Velasquez Olivera, Christo K. Thomas, Walid Saad

Virginia Tech · Worcester Polytechnic Institute

Disentangled Representation Learning Unsupervised Representation Learning Vector Symbolic Architectures

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Disentanglement, the separation of factors of variation in data using neural networks, remains a long-standing challenge in machine learning. Prior work has addressed this problem with variational autoencoders and generative adversarial networks that incorporate ideas from variational inference and information-theoretic constraints. In contrast to methods that rely on continuous representations, we propose a design that treats disentangled representations as symbolic structures, motivated by the compositional relationships among the concepts that make up samples from a distribution. However, learning discrete symbolic structures with neural networks while maintaining differentiability is difficult and often requires complex architectures. To address this, we introduce an unsupervised learning algorithm that uses holographic reduced representations (HRR) for neural disentanglement. We show that the HRR unbinding operation provides an inductive bias for separating factors and yields competitive results against baselines, as measured by latent traversals and disentanglement metrics. We complement these empirical findings with an information-theoretic analysis of the HRR unbinding channel. We prove that unbinding induces approximately independent symbol-value pairs and derive a per-slot capacity bound that quantifies how many distinct symbolic concepts can be reliably encoded, giving a quantitative account of the inductive bias toward disentanglement. The resulting representations differ from standard autoencoder-based models, in that their latent units are vectors that are summed together, rather than scalar dimensions of a low-dimensional latent vector. We show that this HRR representation is more robust to noise than other disentangled representations and maintains reconstruction quality across a range of SNRs.

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