Invariant Representation Learning

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21 papers in the last four weeks, up 110% on the four weeks before. 0.2% of all new papers.

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Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts
Jun 24, 2026Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen +1Domain Generalization Invariant Representation Learning
VinUniversity, Vietnam
Transformation Behavior of Images in Latent Space
Jun 23, 2026Christian Zöllner, Mozzam Motiwala, Aysel Ahadova +4Histopathology Image Classification Invariant Representation Learning
Department of Applied Tumor Biology Institute of Pathology Heidelberg University Hospital · Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120, Heidelberg, Germany · National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany +5
Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
Jun 21, 2026Nisha L. Raichur, Lucas Heublein, Dominik Seuß +2Direction-of-Arrival Estimation Physics-Informed ML
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Center for Artificial Intelligence, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany
Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Jun 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorContrastive Learning Time Series Classification
Norwegian University of Science and Technology, Norway · United States Naval Academy, USA
What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry
Jun 17, 2026Zachary P. BradshawGeometric Representation Learning Lie Group Methods
QodeX Quantum, Inc. Chicago, IL
Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
Jun 16, 2026Takaya KawakatsuTable Structure Recognition Multi-Task Learning
Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda, Tokyo, Japan.
Factorized Neural Operators Decompose Dynamic and Persistent Responses
Jun 15, 2026Hao Tang, Yuechen Duan, Jiongyu Zhu +3Mechanistic Interpretability PDE Operator Learning
School of Medicine, University of Dundee, United Kingdom. · School of Data Science, Fudan University, China. · School of Mathematical Sciences, Fudan University, China. +3
One-Step Generalization Ratio Guided Optimization for Domain Generalization
Jun 15, 2026Sumin Cho, Dongwon Kim, Kwangsu KimDomain Generalization Adaptive Gradient Methods
Department of Computer Science and Engineering, University of Sungkyunkwan, Suwon, Korea.
Sensor-Conditioned Representation Learning via Scene-Relevant Observation Quotients
Jun 15, 2026Yan Jiao, Pin-Han Ho, Limei PengRepresentation Learning Autoencoders
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China · Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada · School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
Is Spurious Correlation Removal Always Learnable?
Jun 11, 2026Yibo Zhou, Bo Li, Hai-Miao Hu +3Spurious Correlation Robustness Invariant Representation Learning
Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China · Hangzhou Innovation Institute of Beihang University, Hangzhou 310051, China +1
Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification
Jun 10, 2026Dong-Woo Kim, Tae-Kyun KimInvariant Representation Learning Person Re-Identification
School of Computing, KAIST, Daejeon, South Korea.
Precision-Aware Illumination-Disentangled Vision Transformer for Spacecraft 6D Pose Estimation
Jun 10, 2026Zongwu Xie, Yifan Yang, Yonglong Zhang +3Vision Transformer Object Pose Estimation
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
Jun 8, 2026Xiao Li, Yixuan Jia, Zekai Zhang +6Self-Supervised Learning Memorization in Generative Models
University of Michigan · 1Ohio State University
Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck
Jun 7, 2026Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier +1Audio Deepfake Detection Invariant Representation Learning
Laboratoire Informatique d’Avignon, Avignon Universite, France · EURECOM, Sophia Antipolis, France
Cross Paraphrastic Invariance Learning for Hallucination Detection
Jun 6, 2026Shanshan Lin, Dongsheng Hong, Sibo Ju +3Contrastive Learning LLM Grounding
1Fuzhou University · 2Harbin Institute of Technology (Shenzhen) · 3The Hong Kong University of Science and Technology (Guangzhou)
Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extraction
Jun 5, 2026G. Longari, J. -C. Alvarez Paiva, A. B. TumpachInvariant Representation Learning Image Classification
DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection
Jun 5, 2026Abhishek Ameta, Sayan Banerjee, Shreyas Pandith +4Vision Foundation Model Adaptation One-Class Classification
Samsung Research Institute, Bangalore, India
Anchor PCA
Jun 4, 2026Benedikt Seiter, Anya Fries, Julius von Kügelgen +1Unsupervised Learning Principal Component Analysis
ETH Zürich
Learning Emotion-discriminative Representations for Zero-Shot Cross-lingual Speech Emotion Recognition
Jun 4, 2026Jinyi Mi, Ding Ma, Tomoki TodaZero-Shot Learning Cross-Lingual Representation Alignment
Graduate School of Informatics, Nagoya University, Japan · Information Technology Center, Nagoya University, Japan
Environment-Robust Representation Learning with Empirical Bayes
Jun 3, 2026Yuli Slavutsky, Matthew Shen, Bohan Wu +1Latent Variable Models Empirical Bayes
Department of Statistics Columbia University New York, NY 10027, USA · Departments of Statistics and Computer Science Columbia University New York, NY 10027, USA
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
Jun 1, 2026Manu Srinath Halvagal, Sebastian Lee, SueYeon ChungMarkov Decision Processes Deep Q-Learning
Department of Physics, Harvard University · 2Kempner Institute, Harvard University · Center for Computational Neuroscience, Flatiron Institute
One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition
May 31, 2026Timur Ismagilov, Shakaiba Majeed, Michael Milford +3Visual Place Recognition Invariant Representation Learning
School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, U.K. · Department of Computer Science and Engineering, Hanyang University, Seoul, South Korea · QUT Centre for Robotics, School of Electrical Engineering and Robotics, Brisbane, QLD 4000, Australia +1
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
May 30, 2026Tong Wu, Andrew Campbell, Anna ScaglioneZero-Shot Learning Transfer Learning
University of Central Florida, USA · Cornell University, USA
Learning Cardiac Latent Representations in Vectorcardiogram Space
May 29, 2026Bosong Huang, Panzhen Zhao, Zengxiang Li +5Representation Learning Electrocardiography
Griffith University, Australia · SingHealth Duke-NUS AI in Medicine Institute, Singapore · SingHealth AI Office, Singapore +1
Learning Permutation-invariant Macroscopic Dynamics
May 29, 2026Zhichao Han, Mengyi Chen, Qianxiao LiDynamical Systems Latent Dynamics Modeling
Institute for Functional Intelligent Materials, National University of Singapore · Department of Mathematics, National University of Singapore
Learning to Perceive the World Through Control: Empowerment-Based Representation Learning
May 28, 2026Mahsa Bastankhah, Sophie Broderick, Benjamin EysenbachRepresentation Learning Reinforcement Learning
Princeton University, USA
Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
May 27, 2026Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4Self-Supervised Learning Siamese Neural Networks
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA · Electrical Engineering, Yale University, New Haven, CT, USA +1
General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
May 27, 2026Huaihai Lyu, Chaofan Chen, Mingyu Cao +2Geometric Representation Learning Vision-Language-Action Models
1MAIS, Institute of Automation, Chinese Academy of Sciences · 2Beijing Academy of Artificial Intelligence.
Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation
May 25, 2026Chunzheng Zhu, Yijun Wang, Jianxin Lin +5Invariant Representation Learning Medical Image Representation Learning
Hunan University, Changsha, China · Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
May 24, 2026Jinjin Chi, Lei Feng, Yiming Wang +4Zero-Shot Time Series Forecasting Time Series Forecasting
College of Computer Science and Technology, Jilin University, Changchun, China · College of Computing and Data Science, Nanyang Technological University, Singapore

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Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen, Kok-Seng Wong

VinUniversity, Vietnam

Domain Generalization Invariant Representation Learning

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Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training. A common approach enforces invariance of representations across all source domains, assuming predictive structure is globally shared. However, we demonstrate that enforcing invariance across more domains gradually restricts the feasible representation space, discarding transferable predictive factors that are not universally shared. To address this limitation, we propose subset-shared invariance, where predictive structure is assumed stable only within domain subsets. We implement this principle with a mixture-of-experts architecture, where each expert aligns the specific domains it serves and a routing mechanism composes subset-invariant components for prediction. This creates a routing-conditioned invariance, jointly learned with the representation. To facilitate effective decomposition, we develop training objectives that encourage selective alignment, confident and balanced routing, and diverse expert specialization. Experiments on DomainBed benchmarks demonstrate improved out-of-domain generalization and greater robustness under increasing domain heterogeneity. Our results suggest that DG should move beyond enforcing a single global invariance and instead model invariance through partially shared structure across domain subsets.

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