Invariant Representation Learning

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Overview

Invariant representation learning studies how to encode data so that changes such as viewpoint, sensor, speaker, environment, or input format do not obscure task-relevant information. Approaches include symmetry-aware architectures, contrastive and predictive self-supervision, and methods that disentangle stable features from domain-specific variation or align representations across environments. These ideas are used in vision, graphs, speech, biomedical signals, robotics, and scientific data, often evaluated on shifted domains, unseen transformations, or cross-subject and cross-device benchmarks. A central tension is that removing variation can improve robustness but also discard useful signal; invariance alone does not guarantee reliable transfer, fairness, or generalization when the target itself changes across environments.

Latest papers

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229 papers33 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.

  1. Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation

    Oct 29, 2026Yansong Bai, Yunzhi Zhuge, Xinzhuo Yu +4Domain Generalization for Semantic SegmentationInvariant Representation Learning

    Yansong Bai, Yunzhi Zhuge, Lu Zhang, Pingping Zhang and Huchuan Lu are with the School of Information and Communication Engineering, Dalian University of Technology, Dalian 116081, China. · Xinzhuo Yu and Jin Zhan are with the School of Computer Science and Technology, Dalian University of Technology, Dalian 116081, China.

  2. Intersectional Invariant Learning for Fair Deepfake Detection

    Oct 27, 2026Uzoamaka Ezeakunne, Taiwo Olabamipe, Deevesh Jain +1Deepfake DetectionIntersectional Fairness

    Department of Computer Science, Florida State University · Department of Computer Science, University of Central Florida

  3. The Standard Deviation in VICReg Buys a Safety Margin

    Oct 23, 2026Hawar DizayeeSelf-Supervised LearningInvariant Representation Learning

    Independent Researcher

  4. SKG in Practice: OOD Generalization Cases Across FourDomains

    Oct 23, 2026Jian An, Chaoshi Wu, Xinpan LuoScientific MLOOD Generalization

  5. Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning

    Oct 15, 2026Guimin Hu, Qiwei Peng, Boyan Xu +1Distribution Shift RobustnessMultimodal Robustness

    Guangdong University of Technology · University of Copenhagen

  6. Monotone Piecewise-Linear Harmonization of Multitemporal Multispectral Surface Reflectance With Unsupervised Deep Multiview Pseudoinvariant Features Learning

    Oct 15, 2026Lino Garda Denaro, Chia-Hsiang LinRemote SensingInvariant Representation Learning

    Research Center for Geoinformatics, National Research and Innovation Agency (BRIN), Cibinong, Indonesia · Department of Electrical Engineering, National Cheng Kung University (NCKU), Tainan, Taiwan

  7. SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning

    Oct 15, 2026Jongann Lee, Sun Woo Park, Yun Young ChoiSelf-Supervised Point Cloud LearningPoint Cloud Learning

    Max Planck Institute of Mathematics Bonn, Germany · SolverX Seoul, Republic of Korea

  8. DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations

    Oct 13, 2026Jun Tian, Yingjie Niu, Ruo Chen +2Quadruped Robot NavigationRobot Navigation

    Deep Robotics · University of Illinois at Urbana-Champaign · Zhejiang University +2

  9. On Making SE(2)\mathrm{SE}(2)-Invariant Networks Optimal

    Oct 12, 2026Tomas Karella, Emily Shinkle, Alice Elisabeth Anastasia Allen +3Invariant Representation LearningEquivariant Neural Networks

    CAI-3: Information Sciences Los Alamos National Laboratory · Machine Learning in Molecular Dynamics Max Planck Institute for Polymer Research · T-5: Applied Mathematics and Plasma Physics Los Alamos National Laboratory

  10. HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning

    Oct 12, 2026Xin Liu, Feng Lu, Wei LiElectroencephalographySelf-Supervised Pre-Training

  11. LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications

    Oct 10, 2026Daniel Sanchez, Djordje Mihajlovic, Federica Valeau +4Invariant Representation LearningMolecular Representation Learning

    Technion - Israel Institute of Technology

  12. DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models

    Oct 10, 2026Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8EEG Foundation ModelsCross-Subject EEG Decoding

    Uploading Inc · College of Computing and Data Science, Nanyang Technological University · Department of Biostatistics, School of Public Health, Peking University +2

  13. SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction

    Oct 8, 2026Ruijin Hua, Zichuan Liu, Zhuokai Zhao +1Representation LearningInvariant Representation Learning

    University of Illinois Urbana-Champaign · Carnegie Mellon University · University of Chicago

  14. A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space

    Oct 8, 2026Antal Jakovác, András TelcsKnowledge RepresentationComputational Cognitive Modeling

    Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, H-1121 Budapest, Hungary · Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fovam Square, H-1093 Budapest, Hungary

  15. GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer

    Oct 8, 2026Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu +3Continuous Glucose MonitoringCross-Domain Transfer Learning

    Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Vitalverse, Abu Dhabi, UAE · Google, USA

  16. How Do Transformers Learn to Represent Symmetries?

    Oct 7, 2026Eduardo Santos-Escriche, Valerie Engelmayer, Ya-Wei Eileen Lin +1Invariant Representation LearningTransformer

    Technical University of Munich Munich Center for Machine Learning · Massachusetts Institute of Technology

  17. Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction

    Oct 6, 2026Yiming Ren, Xiang Liu, Mustafa Hajij +2Materials Property PredictionMolecular Property Prediction

    Department of Mathematics, University of Georgia, Athens, GA 30602, USA. · Department of Data Science, University of San Francisco, CA 94117, USA. · Department of Computer Science and Technology, Cambridge University, Cambridge, United Kingdom. +2

  18. Scale-Invariant Training for Time Series Foundation Models

    Oct 5, 2026Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1Time Series ForecastingInvariant Representation Learning

    Carnegie Mellon University · Amazon∗ · Nixtla

  19. SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

    Oct 5, 2026Shanglin Li, Shiwen Chu, Okan Koç +5Cross-Subject EEG DecodingUnsupervised Domain Adaptation

    BIFOLD, Germany · Technical University of Berlin, Germany · Advanced Telecommunications Research Institute International, Japan +2

  20. dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

    Oct 5, 2026Alfred Nilsson, Joel Lapin, Samuel H. Payne +2Self-Supervised Pre-TrainingMass Spectrometry

    Science for Life Laboratory, KTH Royal Institute of Technology Stockholm, Sweden · Computational Mass Spectrometry, TUM School of Life Sciences Technical University of Munich, Freising, Germany · Biology Department, Brigham Young University Provo, Utah 84602, United States +1

  21. Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis

    Oct 2, 2026Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan +5Geometric Representation LearningSelf-Supervised Visual Representation Learning

    Georgia Institute of Technology

  22. Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions

    Oct 1, 2026Jingyao Zhang, Yuxuan Li, Lu Han +2Information BottleneckInvariant Representation Learning

    School of Computer Science, The University of Sydney Sydney, NSW 2006, Australia

  23. Structure-agnostic Causal Representation Learning

    Oct 1, 2026Arman Behnam, Binghui WangCausal Representation LearningOOD Generalization

    Department of Computer Science Illinois Institute of Technology Chicago, IL, USA

  24. Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces

    Sep 30, 2026Sakin Kirti, Joel ZylberbergImage Corruption RobustnessInvariant Representation Learning

    Bioinformatics IDP, Jules Stein Eye Institute University of California, Los Angeles Los Angeles, CA 90095, USA · Jules Stein Eye Institute University of California, Los Angeles Los Angeles, CA, 90095, USA

  25. scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning

    Sep 30, 2026Yunhak Oh, Yoonho Lee, Junseok Lee +9Variational AutoencodersRepresentation Learning

    KAIST · Work done during an internship at HITS · HITS

  26. Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning

    Sep 29, 2026Anthony Fuller, Scott C. Lowe, Daniel G. Kyrollos +3Masked AutoencodersInvariant Representation Learning

    Vector Institute · Carleton University · University of Guelph +1

  27. Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance

    Sep 29, 2026Fabian A. Mikulasch, Friedemann ZenkeLatent Variable ModelsRepresentation Identifiability

    Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland · Faculty of Science, University of Basel, Switzerland

  28. Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

    Sep 29, 2026Junghyun Kim, Ngseo Kim, ChungWoo Lee +7Domain GeneralizationVision-Language-Action Models

    OpenMind, San Francisco, CA, USA · Seoul National University, Seoul, Korea · Hyundai Motors, Korea +2

  29. Geometry-Conditioned Fixed-Scaffold Encoders for Time-Warp Robust Sequence Retrieval

    Sep 29, 2026Cassandra Yang, Yufan TangTemporal IRInvariant Representation Learning

    New York University · Fudan University

  30. Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models

    Sep 29, 2026Abdullah All Tanvir, Xin ZhongInformation GeometryNeural Representation Geometry

    Department of Computer Science University of Nebraska Omaha, Omaha, NE, USA