Disentangled Representations

CVPR 2024arXiv:2403.06951

#2

DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations

Tianhao Qi, Shancheng Fang, Yanze Wu et al.

text-to-image synthesisstyle transferdisentangled representationscross-attention layers+2

90

ICLR 2024arXiv:2311.04056

#3

Multi-View Causal Representation Learning with Partial Observability

Dingling Yao, Danru Xu, Sébastien Lachapelle et al.

58

CVPR 2025arXiv:2503.16429

#4

Sonata: Self-Supervised Learning of Reliable Point Representations

Xiaoyang Wu, Daniel DeTone, Duncan Frost et al.

44

ICCV 2025arXiv:2504.01017

#5

Scaling Language-Free Visual Representation Learning

David Fan, Shengbang Tong, Jiachen Zhu et al.

visual self-supervised learningcontrastive language-image pretrainingmultimodal representation learningvision encoders+2

41

ICLR 2024arXiv:2401.11430

#6

Disentangled Prompt Representation for Domain Generalization

De Cheng, Zhipeng Xu, XINYANG JIANG et al.

Exploring Diffusion Time-steps for Unsupervised Representation Learning

Zhongqi Yue, Zhongqi Yue, Jiankun Wang et al.

32

CVPR 2025arXiv:2412.03561

#8

FLAIR: VLM with Fine-grained Language-informed Image Representations

Rui Xiao, Sanghwan Kim, Iuliana Georgescu et al.

23

CVPR 2024arXiv:2403.10897

#9

Rethinking Multi-view Representation Learning via Distilled Disentangling

Guanzhou Ke, Bo Wang, Xiao-Li Wang et al.

multi-view representation learningdistilled disentanglingmasked cross-view predictionview-consistent representations+2

23

CVPR 2024arXiv:2403.16398

#10

Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

Xinting Liao, Weiming Liu, Chaochao Chen et al.

18

ICLR 2024arXiv:2307.09912

#11

Learning invariant representations of time-homogeneous stochastic dynamical systems

Vladimir Kostic, Pietro Novelli, Riccardo Grazzi et al.

16

CVPR 2024arXiv:2402.02352

#12

Region-Based Representations Revisited

Michal Shlapentokh-Rothman, Ansel Blume, Yao Xiao et al.

region-based representationssemantic segmentationobject-based image retrievalmulti-image analysis+4

14

AAAI 2024arXiv:2306.03364

#13

Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual Learning

Nicolas Michel, Giovanni Chierchia, Romain Negrel et al.

unit sphere representationsangular gaussian distributionvon mises-fisher distributiononline continual learning+2

14

ICLR 2024arXiv:2308.04428

#14

Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data

Thomas T. Zhang, Leonardo Felipe Toso, James Anderson et al.

14

ICLR 2025arXiv:2504.16929

#15

A Unifying Framework for Representation Learning

Shaden Alshammari, John Hershey, Axel Feldmann et al.

representation learningloss functionsinformation geometrycontrastive learning+4

13

representation disentanglementdiffusion-based autoencoderlatent space navigationunsupervised learning+4

#16

Closed-Loop Unsupervised Representation Disentanglement with $\beta$-VAE Distillation and Diffusion Probabilistic Feedback

Xin Jin, Bohan Li, Baao Xie et al.

ECCV 2024

12

ICLR 2024arXiv:2310.10833

#17

Proper Laplacian Representation Learning

Diego Gomez, Michael Bowling, Marlos C. Machado

11

ICML 2024arXiv:2406.04596

#18

Federated Representation Learning in the Under-Parameterized Regime

Renpu Liu, Cong Shen, Jing Yang

federated representation learningunder-parameterized regimepersonalized federated learninglow-rank matrix approximation+3

11

ICLR 2024arXiv:2212.07699

#19

Retrieval-based Disentangled Representation Learning with Natural Language Supervision

Jiawei Zhou, Xiaoguang Li, Lifeng Shang et al.

10

CVPR 2025arXiv:2412.04077

#20

SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning

Seokju Yun, Seunghye Chae, Dongheon Lee et al.

domain generalizationparameter efficient fine tuningsingular value decompositionsemantic segmentation+4

10

ICML 2024arXiv:2406.18131

#21

Factorized Diffusion Autoencoder for Unsupervised Disentangled Representation Learning

Ancong Wu, Wei-shi Zheng

Sequential Disentanglement by Extracting Static Information From A Single Sequence Element

Nimrod Berman, Ilan Naiman, Idan Arbiv et al.

unsupervised sequential disentanglementstatic factor extractiondynamic factor extractioninformation leakage mitigation+3

9

ICLR 2024arXiv:2305.18484

#23

Neural Fourier Transform: A General Approach to Equivariant Representation Learning

Masanori Koyama, Kenji Fukumizu, Kohei Hayashi et al.

9

ICML 2024arXiv:2402.09142

#24

When Representations Align: Universality in Representation Learning Dynamics

Loek van Rossem, Andrew Saxe

representation learning dynamicseffective theoryencoding mapdecoding map+4

ICML 2025arXiv:2411.07591

#25

Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization

Chenbei Lu, Laixi Shi, Zaiwei Chen et al.

AAAI 2024arXiv:2401.08464

#26

Enhancing Evolving Domain Generalization through Dynamic Latent Representations

Binghui Xie, Yongqiang Chen, Jiaqi Wang et al.

ICLR 2025arXiv:2502.02496

#27

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries

Chris Kolb, Tobias Weber, Bernd Bischl et al.

ICML 2024arXiv:2410.02068

#28

Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits

Jiabin Lin, Shana Moothedath, Namrata Vaswani

contextual banditsmulti-task learningrepresentation learningalternating projected gradient descent+3

ICLR 2025arXiv:2410.23996

#29

An Information Criterion for Controlled Disentanglement of Multimodal Data

Chenyu Wang, Sharut Gupta, Xinyi Zhang et al.

6

ICML 2024arXiv:2503.15355

#30

Robustness of Nonlinear Representation Learning

Simon Buchholz, Bernhard Schölkopf

unsupervised representation learningnonlinear representation learningindependent component analysisnonlinear ica+4

6

ICLR 2025arXiv:2503.00639

#31

Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery

Yanyi Li, Xi Zhang, Yisi Luo et al.

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning

Zijian Li, Shunxing Fan, Yujia Zheng et al.

5

ICLR 2025arXiv:2407.11249

#33

Disentangling Representations through Multi-task Learning

Pantelis Vafidis, Aman Bhargava, Antonio Rangel

disentangled representationsmulti-task learningevidence accumulationcontinuous attractors+4

5

ICCV 2025arXiv:2503.08751

#34

Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning

Qi Wang, Zhipeng Zhang, Baao Xie et al.

disentangled representation learningworld modelsreinforcement learningsemantic knowledge transfer+4

5

ICCV 2025arXiv:2504.20041

#35

Learning Streaming Video Representation via Multitask Training

Yibin Yan, Jilan Xu, Shangzhe Di et al.

streaming video understandingcausal temporal attentionvision transformer adaptationmultitask visual-language alignment+3

4

ECCV 2024arXiv:2408.05599

#36

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Mathieu Simon, Pascal Frossard, Christophe De Vleeschouwer

disentangled representation learningsequential data analysisstatic-dynamic disentanglementcausal relationship modeling+4

4

ICML 2024arXiv:2308.12696

#37

Disentanglement Learning via Topology

Nikita Balabin, Daria Voronkova, Ilya Trofimov et al.

disentangled representationstopological disentanglementunsupervised learningmanifold learning+4

3

ICLR 2025arXiv:2407.07829

#38

Disentangled Representation Learning with the Gromov-Monge Gap

Théo Uscidda, Luca Eyring, Karsten Roth et al.

3

ICCV 2025arXiv:2507.03304

#39

Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations

Hai Huang, Yan Xia, Sashuai Zhou et al.

3

NEURIPS 2025arXiv:2510.17313

#40

Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations

Tal Barami, Nimrod Berman, Ilan Naiman et al.

disentangled representationssequential datamulti-factor analysisvision-language models+4

ICML 2025arXiv:2501.18797

#41

Compositional Generalization via Forced Rendering of Disentangled Latents

Qiyao Liang, Daoyuan Qian, Liu Ziyin et al.

ICML 2024arXiv:2403.03071

#42

On a Neural Implementation of Brenier's Polar Factorization

Nina Vesseron, Marco Cuturi

polar factorization theoremoptimal transport theoryinput convex neural networksmeasure-preserving maps+4

ICML 2024arXiv:2410.11227

#43

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples

Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.

nonlinear representation learningmulti-task learningdependent data analysissample complexity analysis+3

ICCV 2025arXiv:2506.18463

#44

DIP: Unsupervised Dense In-Context Post-training of Visual Representations

Sophia Sirko-Galouchenko, Spyros Gidaris, Antonin Vobecky et al.

AAAI 2024arXiv:2403.00352

#45

Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning

Ruiqian Nai, Zixin Wen, Ji Li et al.

disentangled representationrepresentation learningabstract visual reasoningdownstream tasks+2

unitary representation learninggroup structure discoveryrepresentation theoryfinite groups+4

#46

Discovering Group Structures via Unitary Representation Learning

Dongsung Huh

ICLR 2025

ICCV 2025arXiv:2411.18651

#47

Verbalized Representation Learning for Interpretable Few-Shot Generalization

Cheng-Fu Yang, Da Yin, Wenbo Hu et al.

vision-language modelsfew-shot learningverbalized representation learninginterpretable features+3

AAAI 2025arXiv:2412.08513

#48

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability

Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer et al.

uncertainty estimationrepresentation learning explainabilityimportance score variabilitybernoulli random variables+3

ICCV 2025arXiv:2503.13236

#49

Gradient Extrapolation for Debiased Representation Learning

Ihab Asaad, Maha Shadaydeh, Joachim Denzler

ICML 2025arXiv:2505.01557

#50

Contextures: Representations from Contexts

Runtian Zhai, Kai Yang, Burak VARICI et al.

contexture theoryrepresentation learningsingular functionsexpectation operator+4

ICLR 2024arXiv:2403.06971

#51

A representation-learning game for classes of prediction tasks

Neria Uzan, Nir Weinberger

graph representation learninggraph entropy maximizationgraph neural networksunsupervised learning+3

#52

$\alpha$TC-VAE: On the relationship between Disentanglement and Diversity

Cristian Meo, Louis Mahon, Anirudh Goyal et al.

Object centric architectures enable efficient causal representation learning

Amin Mansouri, Jason Hartford, Yan Zhang et al.

Federated Representation Angle Learning

Liping Yi, Han Yu, Gang Wang et al.

DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior

Ha-Yeong Choi, Sang-Hoon Lee, Seong-Whan Lee

Learning Graph Representation via Graph Entropy Maximization

Ziheng Sun, Xudong Wang, Chris Ding et al.

ICML 2024

NEURIPS 2025arXiv:2510.21402

#57

Disentangled Representation Learning via Modular Compositional Bias

whie jung, Dong Hoon Lee, Seunghoon Hong

disentangled representation learningmodular inductive biascompositional consistency losslatent space remixing+2

ICML 2024arXiv:2311.14649

#58

Learning in Deep Factor Graphs with Gaussian Belief Propagation

Seth Nabarro, Mark van der Wilk, Andrew Davison

gaussian factor graphsbelief propagationdistributed trainingasynchronous training+3

ICML 2025arXiv:2505.20970

#59

Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective

Joonkyu Kim, Yejin Kim, Jy-yong Sohn

NEURIPS 2025arXiv:2504.00220

#60

Can Diffusion Models Disentangle? A Theoretical Perspective

Liming Wang, Muhammad Jehanzeb Mirza, Yishu Gong et al.

diffusion modelsdisentangled representationslatent variable modelssubspace recovery+4

AAAI 2025arXiv:2412.15086

#61

Delegation-Relegation for Boolean Matrix Factorization

Florent Avellaneda, Roger Villemaire

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation

Haoran Liu, Youzhi Luo, Tianxiao Li et al.

AAAI 2025arXiv:2501.11053

#63

Online Laplacian-Based Representation Learning in Reinforcement Learning

Maheed Ahmed, Jayanth Bhargav, Mahsa Ghasemi

Self-Supervised Representation Learning from Arbitrary Scenarios

Zhaowen Li, Yousong Zhu, Zhiyang Chen et al.

FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement

Shanfeng Wang, Jianzhao Li, Zaitian Liu et al.

Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

Linchao Pan, Can Gao, Jie Zhou et al.

ICCV 2025arXiv:2509.08940

#67

On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning

Jiayi Chen, Aidong Zhang

Deep Disentangled Metric Learning

Jinhee Park, Jisoo Park, Dagyeong Na et al.

Discovering Divergent Representations between Text-to-Image Models

Lisa Dunlap, Trevor Darrell, Joseph Gonzalez et al.

text-to-image modelsvisual representation divergenceevolutionary search algorithmprompt concept discovery+3

multi-view representation learningsequential fusion frameworkreinforcement learning integrationpairwise integrator+3

#70

Transportable Representations for Domain Generalization

Kasra Jalaldoust, Elias Bareinboim

SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning

Ren Wang, Haoliang Sun, Yuxiu Lin et al.

CVPR 2025

ICCV 2025arXiv:2507.18758

#72

Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning

Da Wang, Lin Li, Wei Wei et al.

Disentangled Partial Label Learning

Wei-Xuan Bao, Yong Rui, Min-Ling Zhang

Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding

Xu Yan, Jun Yin, Jie Wen

Learning Efficient and Generalizable Human Representation with Human Gaussian Model

Yifan Liu, Shengjun Zhang, Chensheng Dai et al.

#76

Learning Multi-Task Sparse Representation Based on Fisher Information

Yayu Zhang, Yuhua Qian, Guoshuai Ma et al.

CFD: Learning Generalized Molecular Representation via Concept-Enhanced Feedback Disentanglement

Aming Wu, Cheng Deng

Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference

Khalid OUBLAL, Said Ladjal, David Benhaiem et al.

ICLR 2024