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. Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels

    Mar 31, 2026Marcel Tomàs Bernal, Neil Rohit Mallinar, Mikhail BelkinSymmetry BreakingKernel Methods

    CFIS UPC · UC San Diego

  2. Suppressing Non-Semantic Noise in Masked Image Modeling Representations

    Mar 31, 2026Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3Self-Supervised Visual Representation LearningMasked Image Modeling

    University of Oslo · UiT The Arctic University of Norway

  3. RINO: Rotation-Invariant Non-Rigid Correspondences

    Mar 29, 2026Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3Functional MapsNon-Rigid Shape Correspondence

    TUM · MCML · Stanford University +1

  4. On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry

    Mar 29, 2026Mohammad Tinati, Stephen TuSelf-Supervised Pre-TrainingInvariant Representation Learning

    Department of Electrical and Computer Engineering, University of Southern California

  5. When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning

    Mar 26, 2026Yifeng Lin, Aiping Huang, Wenxi Liu +4Class-Incremental LearningPrototype-Based Classification

    Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

  6. Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization

    Mar 26, 2026Haoran Pei, Yuguang Yang, Kexin Liu +2OOD GeneralizationInvariant Representation Learning

    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China · School of Electronic Information Engineering, Beihang University, Beijing 100191, P. R. China · School of Artificial Intelligence, Beihang University, Beijing 100191, P. R. China

  7. Multi-Modal Image Fusion via Intervention-Stable Feature Learning

    Mar 24, 2026Xue Wang, Zheng Guan, Wenhua Qian +2Multi-Source Image FusionMultimodal Robustness

    School of Information Science and Engineering, Yunnan University · School of Artificial Intelligence, Nanyang Normal University · Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University

  8. Generalized multi-object classification and tracking with sparse feature resonator networks

    Mar 23, 2026Lazar Supic, Alec Mullen, E. Paxon FradyObject-Centric Representation LearningMulti-Object Tracking

    Redwood Center for Theoretical Neuroscience, UC Berkeley · Neuromorphic Computing Lab, Intel

  9. Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation

    Mar 23, 2026Armand Rousselot, Joran Wendebourg, Ullrich KötheFeature VisualizationInvariant Representation Learning

    Computer Vision and Learning Lab, Heidelberg University.

  10. GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators

    Mar 17, 2026Mattia Rigotti, Nicholas Thumiger, Thomas FrickGraph Neural OperatorsNeural Operators

    IBM Research

  11. GATS: Gaussian Aware Temporal Scaling Transformer for Invariant 4D Spatio-Temporal Point Cloud Representation

    Mar 17, 2026Jiayi Tian, Jiaze WangPoint Cloud LearningInvariant Representation Learning

    State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Harbin Institute of Technology, Shenzhen, Pengcheng Laboratory

  12. Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition

    Mar 17, 2026Xiaozhou Ye, Feng Jiang, Zihan Wang +3Human Activity RecognitionDomain Generalization

    School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China · School of Cyberspace Security, Nanjing University of Information Science and Technology, Nanjing, China · Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China +1

  13. Make it SING: Analyzing Semantic Invariants in Classifiers

    Mar 15, 2026Harel Yadid, Meir Yossef Levi, Roy Betser +1Explainable Image ClassificationInvariant Representation Learning

    Viterbi Faculty of Electrical and Computer Engineering, Technion – Israel Institute of Technology, Haifa, Israel

  14. VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation

    Mar 13, 2026Juhye Park, Wooju Lee, Dasol Hong +4Cross-View Geo-LocalizationInvariant Representation Learning

    Urban Robotics Lab, School of Electrical Engineering, KAIST · Hanwha Aerospace

  15. Text-Phase Synergy Network with Dual Priors for Unsupervised Cross-Domain Image Retrieval

    Mar 13, 2026Jing Yang, Hui Xue, Shipeng Zhu +1Unsupervised Representation LearningInvariant Representation Learning

    School of Computer Science and Engineering, Southeast University, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China

  16. Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields

    Mar 8, 2026Alejandro García-Castellanos, Gijs Bellaard, Remco Duits +2Equivariant Neural NetworksInvariant Representation Learning

    Amsterdam Machine Learning Lab (AMLab), University of Amsterdam · Department of Mathematics and Computer Science, Eindhoven University of Technology · Leiden Institute of Advanced Computer Science, Universiteit Leiden

  17. Learning Clinical Representations Under Systematic Distribution Shift

    Mar 7, 2026Yuanyun Zhang, Shi LiOOD GeneralizationInvariant Representation Learning

    Columbia University

  18. VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization

    Mar 7, 2026Seul-Ki Yeom, Marcel Simon, Eunbin Lee +1Self-Supervised Visual Representation LearningObject-Centric Representation Learning

    Nota AI GmbH

  19. TransMASK: Masked State Representation through Learned Transformation

    Mar 5, 2026Sagar Parekh, Preston Culbertson, Dylan P. LoseyRepresentation LearningRobot Imitation Learning

    Mechanical Engineering Department, Virginia Tech · Computer Science Department, Cornell University

  20. Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

    Mar 5, 2026Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey +3Human Activity RecognitionDomain-Adversarial Learning

    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden · Faculty of Information Science & Electrical Engineering, Kyushu University, Fukuoka, Japan · RPTU, Kaiserslautern, Germany +2

  21. Soft Equivariance Regularization for Invariant Self-Supervised Learning

    Mar 4, 2026Joohyung Lee, Changhun Kim, Hyunsu Kim +2Self-Supervised Visual Representation LearningEquivariant Representation Learning

    AITRICS · Voronoi Inc. · Korea Advanced Institute of Science and Technology (KAIST)

  22. Coalgebras for categorical deep learning: Representability and universal approximation

    Mar 3, 2026Dragan MašulovićEquivariant Representation LearningInvariant Representation Learning

    University of Novi Sad, Faculty of Sciences Department of Mathematics and Informatics Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia

  23. MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

    Mar 2, 2026Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3Machine Learning Interatomic PotentialsComputational Materials Science

    State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, UCAS

  24. FoV-Net: Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Casting

    Feb 27, 2026Matteo Ballegeer, Dries F. BenoitBoundary Representation Modeling3D Representation Learning

  25. Learning Continuous Wasserstein Barycenter Space for Generalized All-in-One Image Restoration

    Feb 26, 2026Xiaole Tang, Xiaoyi He, Jiayi Xu +2All-in-One Image RestorationOOD Generalization

    School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi, P.R. China.

  26. SO3UFormer: Learning Intrinsic Spherical Features for Rotation-Robust Panoramic Dense Prediction

    Feb 26, 2026Qinfeng Zhu, Yunxi Jiang, Lei FanDepth EstimationVision Transformer

    University of Liverpool, United Kingdom. · CNRS, France. · Xi’an Jiaotong-Liverpool University, China.

  27. Transformers converge to invariant algorithmic cores

    Feb 26, 2026Joshua S. SchiffmanTransformer InterpretabilityInvariant Representation Learning

    New York Genome Center, New York, NY, USA.

  28. Assessing the Impact of Speaker Identity in Speech Spoofing Detection

    Feb 24, 2026Anh-Tuan Dao, Driss Matrouf, Nicholas EvansAudio Deepfake DetectionInvariant Representation Learning

    Laboratoire d’informatique d’Avignon, France · EURECOM, Sophia Antipolis, France

  29. Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning

    Feb 23, 2026Shimeng Huang, Matthew Robinson, Francesco LocatelloInstrumental Variable EstimationCausal Identifiability

    Institute of Science and Technology Austria (ISTA)

  30. Toward Manifest Relationality in Transformers via Symmetry Reduction

    Feb 21, 2026J. François, L. RaveraTransformerTransformer Attention

    Mons University (UMONS), 20 Place du Parc, 7000 Mons, Belgium. · Grupo de Investigación en Física Teórica, Universidad Católica De La Santísima Concepción, Alonso de Ribera 2850, Concepción, Chile.