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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Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models
May 21, 2026Piotr Kubaty, Patryk Marszałek, Łukasz Struski +3Disentangled Representation Learning Multimodal Disentangled Representation Learning
Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
Disentanglement Beyond Generative Models with Riemannian ICA
May 21, 2026Edmond CunninghamDisentangled Representation Learning Independent Component Analysis
University of Massachusetts Amherst
Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
May 21, 2026Julian Gutheil, Simon Hitzginger, Robert LegensteinNeural Network Generalization Disentangled Representation Learning
Graz University of Technology · Institute of Machine Learning and Neural Computation Graz University of Technology Graz, Austria
Deep Attention Reweighting: Post-Hoc Attention-Based Feature Aggregation in CNNs for Disentangling Core and Spurious Features under Spurious Correlations
May 20, 2026Kin Whye Chew, Jingxian WangDisentangled Representation Learning Spurious Correlation Robustness
National University of Singapore, Singapore 117583, Singapore
Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
May 20, 2026Tong Wang, Yiqing Xu, Leo Yang YangDisentangled Representation Learning Interpretable ML
School of Management Yale University New Haven, CT, US, 06511 · Department of Accountancy, Economics and Finance Hong Kong Baptist University Kowloon, Hong Kong SAR
TriForces: Augmenting Atomistic GNNs for Transferable Representations
May 20, 2026Ali Ramlaoui, Alexandre Duval, Hannah Bull +4Disentangled Representation Learning Machine Learning Interatomic Potentials
Entalpic, Paris, France · Universit´e Paris-Saclay, CentraleSup´elec, Inria, Gif-sur-Yvette, France
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
May 19, 2026Hongjiang Chen, Xin Zheng, Pengfei Jiao +5Disentangled Representation Learning Temporal GNNs
Hangzhou Dianzi University, Hangzhou, China · RMIT University, Melbourne, Australia · Tianjin University, Tianjin, China +1
What Makes a Representation Good for Single-Cell Perturbation Prediction?
May 19, 2026Wenkang Jiang, Yuhang Liu, Yichao Cai +5Variational Autoencoders Disentangled Representation Learning
Australian Institute for Machine Learning, Adelaide University, Australia · Responsible AI Research Centre, Australia · College of Computer Science and Artificial Intelligence, Fudan University, China +3
Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling
May 18, 2026Yihang Wu, Yihang Sun, Shaofeng Zhang +4Disentangled Representation Learning Vision Transformer
Institute of Trustworthy Embodied Artificial Intelligence (TEAI), Fudan University · 3Sch. of Artificial Intelligence & Sch. of Computer Science, Shanghai Jiao Tong University · University of Science and Technology of China +1
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
May 18, 2026Yuanyun Zhang, Shi LiDisentangled Representation Learning Representation Learning
University of the Chinese Academy of Sciences · Columbia University
Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives
May 16, 2026Santosh PremiDisentangled Representation Learning Latent Dynamics Modeling
Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding
May 16, 2026Xiang Gao, Hui Tian, Yanming Zhu +2Disentangled Representation Learning Cross-Modal Representation Learning
School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, 4222, QLD, Australia
Isolating Nonlinear Independent Sources in fMRI with $β$ -TCVAE Models
May 15, 2026Qiang Li, Shujian Yu, Jesus Malo +3Variational Autoencoders Disentangled Representation Learning
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA · Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands · 3Image Processing Laboratory, University of Valencia, Valencia, Spain +2
Controlla: Learning Controllability via Graph-Constrained Latent Geometry
May 15, 2026Jamuna S. Murthy, Amin Karimi Monsefi, Rajiv RamnathDisentangled Representation Learning Multimodal Disentangled Representation Learning
Ramaiah Institute of Technology · The Ohio State University
DiLA: Disentangled Latent Action World Models
May 15, 2026Tianqiu Zhang, Muyang Lyu, Yufan Zhang +2Action-Conditioned Video Generation Latent Action Learning
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, IDG/McGovern Institute for Brain Research, Peking University. · Center of Quantitative Biology, Peking University. · School of Psychological and Cognitive Sciences, Key Laboratory of Machine Perception (Ministry of Education), Peking University.
Learning Disentangled Representations for Generalized Multi-view Clustering
May 15, 2026Xin Zou, Ruimeng Liu, Chang Tang +4Disentangled Representation Learning Multi-View Learning
School of Computing and Information Technology, University of Wollongong, NSW, 2500, Australia · School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Why Latent Actions Fail, and How to Prevent It
May 13, 2026Jung Min Lee, Taehyun Cho, Li Zhao +1Latent Action Learning Disentangled Representation Learning
Seoul National University · Microsoft Research
VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
May 13, 2026Yuxuan Weng, Wenhan Luo, Qijia ShaoMissing-Modality Learning Disentangled Representation Learning
The Hong Kong University of Science and Technology (HKUST)
From Generalist to Specialist Representation
May 12, 2026Yujia Zheng, Fan Feng, Yuke Li +3Unsupervised Learning Disentangled Representation Learning
1CMU · 2UIUC · 3UCSD +3
MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation
May 12, 2026Sonali Godavarthy, Matthias Neuwirth-Trapp, Tim-Felix Faasch +3Disentangled Representation Learning Compositional Image Generation
Bosch Research, Hildesheim, Germany · ETH Zürich, Zürich, Switzerland · University of Siegen, Siegen, Germany +1
Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning
May 12, 2026Hyeonjin Kim, Hangyeol Jung, Heechan Yun +2Disentangled Representation Learning Sparse Autoencoders
Yonsei University · Kookmin University
Structured Coupling for Flow Matching
May 8, 2026Xavier Sumba, Carles Balsells-Rodas, Yingzhen LiFlow Matching Disentangled Representation Learning
Imperial College London
UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
May 8, 2026Shuai Zhang, Zhecheng Shi, Zhuxiao Li +4Disentangled Representation Learning Multimodal Robustness
1HKUST(GZ) · 2HKUST
Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection
May 8, 2026Zhiyuan Wang, Yanxiang Chen, Pengcheng Zhao +2Disentangled Representation Learning Domain Generalization
Hefei University of Technology · Key Laboratory of Knowledge Engineering with Big Data · Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education; School of Computer Science and Information Engineering, Hefei University of Technology; and Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) +3
Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
May 7, 2026Minheng Chen, Chao Cao, Jing Zhang +2Multi-View Consistency Disentangled Representation Learning
Department of Computer Science and Engineering, University of Texas at Arlington, United States · School of Computing, University of Georgia, United States
Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement
May 7, 2026Roussel Desmond Nzoyem, Mauro ComiDisentangled Representation Learning Implicit Neural Representations
Department of Computer Science University of Manchester Manchester, M13 9PL · Department of Computer Science University of Bristol Bristol, BS8 1QU
Metonymy in vision models undermines attention-based interpretability
May 7, 2026Ananthu Aniraj, Cassio F. Dantas, Dino Ienco +2Transformer Interpretability Disentangled Representation Learning
Inria, EVERGREEN, University of Montpellier, 34090 Montpellier, France · INRAE, UMR TETIS, University of Montpellier, 34090 Montpellier, France · University of Trento, Trento, Italy
A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
May 7, 2026Motasem Alkayid, Zhengsen Xu, Saeid Taleghanidoozdoozan +7Remote Sensing Image Understanding Mamba
Department of Geomatics Engineering, University of Calgary, Canada
Deep Dreams Are Made of This: Visualizing Monosemantic Features in Diffusion Models
May 6, 2026Adam Szokalski, Mateusz ModrzejewskiDisentangled Representation Learning Latent Diffusion Models
Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland
Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction
May 5, 2026Qing Wu, Xuanyu Tian, Chenhe Du +4Disentangled Representation Learning Implicit Neural Representations
Medical AI Lab, Ant Group · ShanghaiTech University · Shanghai Jiao Tong University

May 21, 2026, cs.CVJ/K move · Enter open · S save

Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models

Piotr Kubaty, Patryk Marszałek, Łukasz Struski, Adam Wróbel+2

Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland

Disentangled Representation Learning Multimodal Disentangled Representation Learning VLM Interpretability Sparse Autoencoders Multimodal Embedding

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Vision-language models learn powerful multimodal embeddings, yet their internal semantics remain opaque. While sparse autoencoders (SAEs) can extract interpretable features, they rely on expanding the representation dimension, which compromises the original geometry and introduces redundancy. We introduce CEDAR (Conceptual Embedding Disentanglement via Adaptive Rotation), a post-hoc method that reveals the compositional structure of pretrained embeddings without increasing dimensionality. By learning an invertible transformation with a top-

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sparsity bottleneck, CEDAR concentrates semantic information into axis-aligned disentangled coordinates. In CLIP-like architecture, individual coordinates can be interpreted with textual concepts, while for generative models such as BLIP, they can be decoded into natural language descriptions. Experiments demonstrate that CEDAR achieves a competitive reconstruction-sparsity trade-off while producing explanations that are more interpretable and better aligned with human perception. Our results suggest that the apparent entanglement in vision-language representations can be resolved through a suitable change of basis, eliminating the need for overcomplete expansions.

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