Metric Learning

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Global Average Precision for Representation Learning
Oct 7, 2026Bill Psomas, Mohammad Mahdi, Michalis Thomas +3Learning to Rank Metric Learning
VRG, FEE, Czech Technical University in Prague · INSAIT, Sofia University “St. Kliment Ohridski”
Directed Temporal Representations for Offline Visual Control
Oct 6, 2026Chenyang Yuan, Haoyu Wang, Zhuo Sun +1Visual RL Trajectory Representation Learning
University of Sheffield · Shanghai Jiao Tong University · Shanghai University of Finance and Economics +1
METRO: Metric-Enhanced Token Routing Operator
Oct 4, 2026Nodens Koren, Thomas Hofmann, Georgios KissasMetric Learning PDE Operator Learning
Department of Computer Science ETH Zürich Zürich, Switzerland · Swiss Data Science Center ETH Zürich Zürich, Switzerland
Learning Goal-Reaching Quasimetric Geometry From Finite-Time Reachability
Sep 30, 2026Daisuke Yamada, Travis Pence, Vikas SinghMetric Learning Offline RL
Department of Computer Science University of Wisconsin–Madison · Department of Biostatistics and Medical Informatics University of Wisconsin–Madison
Rethinking Automated Voice Similarity by Shifting from EER to Embedding Geometry
Sep 27, 2026Szu-Chi Chen, Jia-Kai Dong, Yi-Cheng Lin +2Audio Representation Learning Speaker Verification
National Taiwan University, Taipei, Taiwan · NVIDIA, Taiwan · Artificial Intelligence Center of Research Excellence (NTU AI-CoRE), NTU, Taiwan
Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
Sep 23, 2026Andrew Bond, Ege Erdem Özlü, Tuna Çimen +4Vision Transformer Geometric Representation Learning
Imperial College London London, UK · Hacettepe University Ankara, Türkiye
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
Sep 15, 2026Hojin Lee, Youngim Nam, Sanghun Lee +1Multi-Agent Trajectory Prediction Autonomous Racing
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs
Sep 14, 2026Sarra Madad, Myriam Maumy, Fr{é}d{é}ric Bertrand +1Predictive Process Monitoring Process Mining
UTT, LIST3N - OPTI, QAD · LIST3N Lab, AI Lab · University of Technology of Troyes (UTT), QAD +11
Learning Kernels by Alignment for Multiclass Bayes Classification
Sep 6, 2026Hollan Haule, Javier EscuderoBayesian Inference Metric Learning
Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)
Sep 2, 2026El Ouanas Belabbaci, Bhavesh Wani, Philipp TerhörstFace Recognition Metric Learning
Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants (LISIC), Faculty of Electrical Engineering, University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria · Johannes Gutenberg University Mainz, Germany
GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering
Aug 8, 2026Zihua Yang, Zhencheng Xie, Junyang Chen +4Clustering Unsupervised Clustering
Guangdong University of Technology · Tsinghua University · Peking University +2
Algorithmic statistics of retinal images
Aug 6, 2026Loan Huynh, Ronald Zambrano, Layton Aho +4Medical Imaging Longitudinal Medical Image Analysis
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA · Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA · School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA +2
Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
Jul 28, 2026Adarsh Bhandary Panambur, Siming Bayer, Andreas MaierMetric Learning Medical Image Classification
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Siemens Healthineers, Erlangen, Germany
PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning
Jul 20, 2026Daegyeong Roh, Juho Bae, Han-Lim ChoiMahalanobis Distance Metric Learning
Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daejon, Republic of Korea.
Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels
Jul 20, 2026Wenxiao Fan, Kan LiMetric Learning Learning with Noisy Labels
School of Computer Science, Beijing Institute of Technology
Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning
Jul 20, 2026Kai Jiang, Zisong Lin, Hongyuan Zhang +2Class-Incremental Learning Metric Learning
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China · The University of Hong Kong, Hong Kong SAR, China · Institute of Artificial Intelligence (TeleAI) of China Telecom, China
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
Jul 14, 2026Xing Zhang, Guanghui Wang, Yanwei Cui +4LLM Agent Evaluation LLM Agent Self-Improvement
1AWS Generative AI Innovation Center · 2HSBC Holdings Plc., HSBC Technology Center, China
Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition
Jul 9, 2026Jing Jie Tan, Ban-Hoe Kwan, Danny Wee-Kiat Ng +6Personality Modeling in Language Models Metric Learning
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia · Institute of Communication Studies, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Management, National Yang Ming Chiao Tung University, Taiwan +2
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
Jul 6, 2026Yiqun Zhang, Yiu-ming CheungClustering Metric Learning
PIEFS: Physics-Informed Eigenfunction Features with Learnable Scaling
Jul 4, 2026Varvara Nazarenko, Timur Lidzhiev, Alexander TarakanovSpectral Regularization Representation Learning
Faculty of Computer Science, HSE University, Moscow, Russia · AI VK, Moscow, Russia
MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning
Jul 3, 2026Yaniv Shulman, Shaghayegh Akbarpour, Jack B. MuirMasked Autoencoders Graph Representation Learning
Fleet Space Technologies, Adelaide, Australia · Research School of Earth Sciences, Australian National University, Acton, Australia
CouCE: A Unified Causal Framework for Debiased Deep Metric Learning
Jun 29, 2026Xin Yuan, Zhenyang Niu, Meiqi Wan +3Debiased ML Causal Representation Learning
Wuhan University of Science and Technology Wuhan, China · Harbin Institute of Technology Harbin, China
Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation
Jun 17, 2026Chenghao XuGeometric Representation Learning Metric Learning
School of Artificial Intelligence and Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, Hunan, China
Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
Jun 13, 2026Shubhang Bhatnagar, Dheeraj Baiju, Narendra AhujaVisual Representation Learning Fine-Grained Image Retrieval
University of Illinois Urbana-Champaign
Riemannian Metric Matching for Scalable Geometric Modeling of Distributions
Jun 12, 2026Jacob Bamberger, Adam Gosztolai, Pierre Vandergheynst +2Geometric Representation Learning Metric Learning
Department of Computer Science, University of Oxford, Oxford, UK · Institute of Artificial Intelligence, Medical University of Vienna, Vienna, Austria · EPFL, Lausanne, Switzerland +1
Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
Jun 10, 2026Yoon Tae Kim, Heejoon Koo, Miika Toikkanen +1OOD Generalization Metric Learning
RSC LAB, MODULABS, Republic of Korea · Department of Electronic Engineering, Wonkwang University, Republic of Korea · AI Convergence Research Institute, Wonkwang University, Republic of Korea
The Hidden Cost of Pairwise Verification in Synthetic Speech Source Tracing
Jun 10, 2026Anton Firc, Zbyněk Lička, Vojtěch Staněk +1Representation Geometry Speaker Verification
Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
May 22, 2026Conlan Olson, Linjun Zhang, Zhun Deng +1Mahalanobis Distance Algorithmic Fairness
Columbia University · Rutgers University · UNC Chapel Hill +1
High Quality Embeddings for Horn Logic Reasoning
May 19, 2026Yifan Zhang, Yasir White, Dean Clark +4Logical Reasoning Metric Learning
Lehigh University, Computer Science and Engineering, 113 Research Dr., Bethlehem, PA, 18015 · Los Angeles Pierce College, Computer Science, 6201 Winnetka Ave, Woodland Hills, CA 91367 · Colorado College, Computer Science, 14 E Cache La Poudre St, Colorado Springs, CO 80903
Few-Shot Network Intrusion Detection Using Online Triplet Mining
May 17, 2026Jack Wilkie, Hanan Hindy, Christos Tachtatzis +2Network Intrusion Detection Few-Shot Learning
Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK · Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt · Faculty of Electrical Engineering, Czech Technical University, 160 00 Prague, Czech Republic

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

Global Average Precision for Representation Learning

Bill Psomas, Mohammad Mahdi, Michalis Thomas, Danda Pani Paudel+2

VRG, FEE, Czech Technical University in Prague · INSAIT, Sofia University “St. Kliment Ohridski”

Learning to Rank Metric Learning Self-Supervised Learning

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Standard information retrieval metrics, such as mean Average Precision (mAP), assess performance one query at a time, based on how the similarities between a query and its positives compare against those with its negatives. The same holds for common representation learning losses, such as InfoNCE and per-query AP surrogates. None of them considers whether similarities are comparable across queries, which any system with a single decision threshold relies on. Global Average Precision (gAP) does, by ranking all query-candidate pairs in one list and computing a single AP. We introduce gSAP, a differentiable surrogate of gAP. It needs only a similarity matrix and a binary matrix marking the positive pairs, the same input as existing losses, so it is a drop-in replacement for them and agnostic to the encoder, the modality, and the source of supervision. Since it considers all possible pairwise comparisons in the batch jointly, it also remains trainable at low temperatures, a regime where per-query surrogates run out of gradient. Swapping it into established recipes improves supervised metric learning, cross-modal alignment, and self-supervised pretraining, where, to our knowledge, it is the first ranking loss to replace the community standard InfoNCE in the latter two. Its similarities are more consistent across queries, which drives the gains under a universal threshold. gSAP retrieves up to four times as many positive pairs as the strongest AP surrogate at the same precision, and it degrades the least when queries with no positives in the database are added. Beyond thresholding, models trained with gSAP also learn better representations, with higher transfer,

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NN and zero-shot classification accuracy.

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