Contrastive Learning

Ask about this topic

Overview

Contrastive learning teaches a model which examples should have similar representations and which should be distinct, often using different views of the same item as positive pairs. Common approaches include InfoNCE methods such as SimCLR and MoCo, supervised and prototype-based losses, hard-negative mining, and CLIP-style alignment across modalities. These representations support classification, retrieval, and transfer when labels are scarce, across images, language, time series, graphs, and scientific data. A central challenge is choosing positives and negatives that capture useful invariances without treating semantically related examples as negatives or discarding information needed for downstream tasks; theory also studies representation collapse, geometry, and generalization.

Latest papers

CardsList

318 papers37 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

  1. Correspondence Is Not Transfer: A Pairing-Controlled Audit of Clinical Audio-Metadata Alignment

    Oct 26, 2026Liu He, Xi Wang, Jiahong Yuan +1Cross-Modal AlignmentAudio Understanding

    University of Science and Technology of China, China · Independent Researcher · University of Cambridge, UK

  2. Meta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference

    Oct 24, 2026Haozhe Luo, Nirosh Sivanesan, Shelley Zixin Shu +2Medical VLMsContrastive Learning for VLMs

    University of Bern, Bern, Switzerland · ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland

  3. Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model

    Oct 23, 2026Yalda Taheri, Mohammad Hassan Heydari, Armon RasooliText Embedding ModelsInformation Retrieval

    Faculty of Engineering, Azad University · Faculty of Computer Engineering, University of Isfahan · Department of Electrical Engineering, Iran University of Science and Technology

  4. Internal Discrepancy for Contrastive Learning: Generalization Bounds and Downstream Behaviors

    Oct 23, 2026Jialiang Wang, Qing Ma, Xiong Zhou +3Contrastive LearningStatistical Learning Theory

    Harbin Institute of Technology, Harbin, China · City University of Hong Kong, Hong Kong, China

  5. Rethinking Representation Geometry in Supervised Contrastive Learning under Long-Tailed Data

    Oct 23, 2026Joonhyeok Jeong, Jy-yong Sohn, Kibok LeeContrastive LearningSupervised Contrastive Learning

    Department of Statistics and Data Science, Yonsei University

  6. IFC-SSL-Bench: Benchmarking Self-Supervised Learning for Annotation-Efficient Multispectral Imaging Flow Cytometry

    Oct 22, 2026Angelo Jovin Yamachui Sitcheu, Roxana-Teodora Fetescu, Thomas Lautenschlager +6Self-Supervised LearningSelf-Supervised Visual Representation Learning

    Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany · Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany · Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland +5

  7. PAIR: Preference-Aligned Intent Retrieval for Premium People Recommendation at LinkedIn

    Oct 22, 2026Zhongkai Shangguan, Yanbin Kang, Yihan Cao +7Recommender SystemsPreference Modeling

    LinkedIn, Sunnyvale, CA, USA

  8. A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners

    Oct 20, 2026Rakesh SenguptaAutomata LearningStatistical Learning Theory

    School of Interwoven Arts and Sciences Krea University Sri City, AP 517646, India

  9. Operator-Mismatch Contrastive Learning for World-Relative Trajectory Coherence

    Oct 18, 2026One Octadion, Firmansyah Sundana, Novanto YudistiraWorld Model EvaluationContrastive Learning

    Faculty of Computer Science, Universitas Brawijaya, Indonesia

  10. Contrastive Hypergraph Source-free Domain Adaptive Object Detection in Adverse Weather Conditions--Extended Version

    Oct 17, 2026Jicheng Yuan, Tung Kieu, Manh Nguyen-Duc +2Object DetectionSource-Free Domain Adaptation

    Open Distributed Systems, Technical University of Berlin · Department of Computer Science, Aalborg University, Denmark · Fraunhofer Institute for Open Communication Systems, Berlin, Germany

  11. VTrajEmbed: Aligning Text with Video Object Trajectories via Spatiotemporal Embeddings

    Oct 16, 2026Ruizhi Chen, Zining Fan, Yudi Guo +5Multimodal EmbeddingVision-Language Retrieval

    Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd. · Peking University · Nanjing University +1

  12. SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis

    Oct 15, 2026Ting Liu, Haoran Lai, HaobinChen +2Radiology VLMsContrastive Learning

    School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P.R. China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P.R. China

  13. Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry

    Oct 15, 2026Lourenço A. Rodrigues, Felix PutzeTemporal Representation LearningSelf-Supervised Time Series Representation Learning

    Cognitive Systems Lab FB3, Department of Mathematics and Computer Science, University of Bremen Bremen, 28359 Bremen, Germany

  14. Hyperspherical Contrastive Representations Unlock Planning and Exploration

    Oct 14, 2026Jonghae Park, Sungil Son, Jonghun Shin +3Robotic RLRobot Motion Planning

    Seoul National University · Princeton University

  15. Dataset-Level Regularization for Joint-Embedding Self-Supervised Learning

    Oct 14, 2026Konstantinos Almpanakis, Anna KreshukSelf-Supervised Visual Representation LearningRepresentation Learning

    European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

  16. TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series

    Oct 14, 2026Aditya Radhakrishnan, Maxwell A. Xu, Wanting Mao +3Self-Supervised Time Series Representation LearningContrastive Learning

    Health Care Engineering Systems Center, University of Illinois Urbana-Champaign · Google · Department of Computer Science, University of Memphis

  17. Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation

    Oct 11, 2026Yuling Jiao, Lican Kang, Jerry Zhijian Yang +2Unsupervised Domain AdaptationRepresentation Learning

  18. Approximation in Contrastive Representation Learning

    Oct 11, 2026Yuanfan Li, Zihan Zhang, Yiming Ying +1Contrastive LearningMultimodal Contrastive Learning

    School of mathematics and statistics, University of Sydney

  19. Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

    Oct 10, 2026Francisco J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa +1Contrastive LearningUncertainty Calibration

    University of Granada · Aalborg University

  20. QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition

    Oct 9, 2026Nanzhu Chen, Feixiang He, Yunfeng Diao +2Adversarial RobustnessSkeleton-Based Action Recognition

    University of Leeds, Leeds, United Kingdom · Central South University, Changsha, China · Hefei University of Technology, Hefei, China +1

  21. ConClick: Element-Wise Contrastive Learning Improves GUI Grounding

    Oct 9, 2026Ruotian Zhang, Yuhui Zhang, Junzhe Huang +3GUI GroundingGUI Agents

  22. Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

    Oct 8, 2026Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz +4Representation LearningContrastive Learning

    Exin Therapeutics, Inc., 2261 Market Street STE 22614, San Francisco, California 94114, United States

  23. Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss

    Oct 7, 2026Ruoyu Zhao, Yuting Chen, Jinheng Zhang +2Contrastive LearningMulti-View Contrastive Learning

    City University of Hong Kong · Georgia Institute of Technology · University of Pennsylvania +2

  24. MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

    Oct 7, 2026Marius Bock, Yuwei Zhang, Juergen Gall +3Human Activity RecognitionMotion Representation Learning

    University of Bonn · University of Cambridge · Lamarr Institute for Machine Learning and Artificial Intelligence +1

  25. What Makes Synthetic Hard Negatives Work in Vision-Language Pretraining?

    Oct 7, 2026Nikos Giakoumoglou, Paschalis Giakoumoglou, Andreas Floros +2Vision-Language PretrainingContrastive Learning

    Imperial College London, London, UK · Centre for Research and Technology Hellas, Thessaloniki, Greece

  26. CLM-as-a-Judge: Evaluating an Open Contrastive Decision Model on Public Judge Benchmarks

    Oct 5, 2026Gowthamkumar NandakishoreContrastive LearningLLM Evaluation

    Independent researcher

  27. Where Does the Semantic Gain Come From? A Reproduction and Extension of Semantic Knowledge-driven Contrastive Learning for Long-Tailed Recognition

    Oct 2, 2026Sushrut GhimireContrastive LearningLong-Tailed Classification

    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

  28. Anchor Divergence for Semantic Geometry in Contrastive Learning

    Oct 2, 2026Akash Kannan, Kiho Park, Victor VeitchNeural Representation GeometryContrastive Learning

    University of Chicago

  29. Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role

    Oct 2, 2026Hubert Huang, Michelle McCleod, Brendan Ames +1Neural Representation GeometryContrastive Learning

    Stanford University, Palo Alto, CA 94305, USA · University of Alabama, Tuscaloosa, AL 35401, USA · University of Southampton, Southampton SO17 1BJ, UK

  30. Reformulation-Contrastive Learning for Mixed Integer Programs

    Sep 30, 2026Ousema Bouaneni, Mathis Le Bail, Clément Elliker +2Contrastive LearningRepresentation Learning

    LIX (École Polytechnique, IP Paris, CNRS) · AMIAD (Agence Ministérielle pour l’IA de Défense)