{"total":102,"rows":[{"id":"2106.05731","title":"Leveraged Weighted Loss for Partial Label Learning","created":"2021-06-10","cited_by_count":66,"area":null},{"id":"2110.12911","title":"Instance-Dependent Partial Label Learning","created":"2021-10-25","cited_by_count":49,"area":null},{"id":"2209.10365","title":"SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning","created":"2022-09-21","cited_by_count":31,"area":null},{"id":"2305.14095","title":"S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions","created":"2023-05-23","cited_by_count":30,"area":"mllm"},{"id":"2202.05613","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","created":"2022-02-11","cited_by_count":24,"area":null},{"id":"2206.00830","title":"Progressive Purification for Instance-Dependent Partial Label Learning","created":"2022-06-02","cited_by_count":23,"area":null},{"id":"2206.00517","title":"One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement","created":"2022-06-01","cited_by_count":22,"area":null},{"id":"2302.05080","title":"Long-Tailed Partial Label Learning via Dynamic Rebalancing","created":"2023-02-10","cited_by_count":22,"area":null},{"id":"2108.09805","title":"Efficient Algorithms for Learning from Coarse Labels","created":"2021-08-22","cited_by_count":21,"area":null},{"id":"2201.08984","title":"PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning","created":"2022-01-22","cited_by_count":21,"area":null},{"id":"2106.06152","title":"On the Robustness of Average Losses for Partial-Label Learning","created":"2021-06-11","cited_by_count":20,"area":null},{"id":"2102.06879","title":"Learning from Similarity-Confidence Data","created":"2021-02-13","cited_by_count":19,"area":null},{"id":"2306.13796","title":"On Learning Latent Models with Multi-Instance Weak Supervision","created":"2023-06-23","cited_by_count":13,"area":null},{"id":"2102.00678","title":"Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification","created":"2021-02-01","cited_by_count":12,"area":null},{"id":"2502.10184","title":"Realistic Evaluation of Deep Partial-Label Learning Algorithms","created":"2025-02-14","cited_by_count":12,"area":null},{"id":"2303.10365","title":"CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning","created":"2023-03-18","cited_by_count":11,"area":null},{"id":"2305.06080","title":"Towards Effective Visual Representations for Partial-Label Learning","created":"2023-05-10","cited_by_count":11,"area":null},{"id":"2305.16912","title":"Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning","created":"2023-05-26","cited_by_count":10,"area":null},{"id":"2311.15502","title":"Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical","created":"2023-11-27","cited_by_count":10,"area":null},{"id":"2211.13655","title":"Learning with Partial Labels from Semi-supervised Perspective","created":"2022-11-24","cited_by_count":9,"area":null},{"id":"2212.08997","title":"Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision","created":"2022-12-18","cited_by_count":9,"area":null},{"id":"2301.12077","title":"ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning","created":"2023-01-28","cited_by_count":9,"area":null},{"id":"2305.12715","title":"Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations","created":"2023-05-22","cited_by_count":9,"area":null},{"id":"2309.09319","title":"Active Learning for Semantic Segmentation with Multi-class Label Query","created":"2023-09-17","cited_by_count":9,"area":null},{"id":"2112.12303","title":"Learning with Proper Partial Labels","created":"2021-12-23","cited_by_count":8,"area":null},{"id":"2302.08155","title":"Learning From Biased Soft Labels","created":"2023-02-16","cited_by_count":8,"area":"distill"},{"id":"2209.14189","title":"Class-Imbalanced Complementary-Label Learning via Weighted Loss","created":"2022-09-28","cited_by_count":7,"area":null},{"id":"2402.01922","title":"A General Framework for Learning from Weak Supervision","created":"2024-02-02","cited_by_count":7,"area":null},{"id":"2412.05029","title":"Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning","created":"2024-12-06","cited_by_count":7,"area":null},{"id":"2306.10458","title":"Weakly Supervised Regression with Interval Targets","created":"2023-06-18","cited_by_count":6,"area":null},{"id":"2407.07638","title":"Tuning Vision-Language Models with Candidate Labels by Prompt Alignment","created":"2024-07-10","cited_by_count":6,"area":"mllm"},{"id":"2306.01191","title":"Conformal Prediction with Partially Labeled Data","created":"2023-06-01","cited_by_count":5,"area":null},{"id":"2309.08216","title":"Unified Risk Analysis for Weakly Supervised Learning","created":"2023-09-15","cited_by_count":5,"area":null},{"id":"2501.12597","title":"Multi-Instance Partial-Label Learning with Margin Adjustment","created":"2025-01-22","cited_by_count":5,"area":null},{"id":"2106.08864","title":"Multi-Class Classification from Single-Class Data with Confidences","created":"2021-06-16","cited_by_count":3,"area":null},{"id":"2209.15402","title":"Rethinking the Learning Paradigm for Facial Expression Recognition","created":"2022-09-30","cited_by_count":3,"area":null},{"id":"2211.04774","title":"IRNet: Iterative Refinement Network for Noisy Partial Label Learning","created":"2022-11-09","cited_by_count":3,"area":null},{"id":"2302.09891","title":"Unreliable Partial Label Learning with Recursive Separation","created":"2023-02-20","cited_by_count":3,"area":null},{"id":"2302.13170","title":"Partial Label Learning for Emotion Recognition from EEG","created":"2023-02-25","cited_by_count":3,"area":null},{"id":"2305.12485","title":"A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition","created":"2023-05-21","cited_by_count":3,"area":null},{"id":"2306.08968","title":"Partial-Label Regression","created":"2023-06-15","cited_by_count":3,"area":null},{"id":"2402.00592","title":"Partial-Label Learning with a Reject Option","created":"2024-02-01","cited_by_count":3,"area":null},{"id":"2408.14369","title":"Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning","created":"2024-08-26","cited_by_count":3,"area":null},{"id":"2203.12836","title":"Learning from Label Proportions with Instance-wise Consistency","created":"2022-03-24","cited_by_count":2,"area":null},{"id":"2210.11194","title":"Controller-Guided Partial Label Consistency Regularization with Unlabeled Data","created":"2022-10-20","cited_by_count":2,"area":null},{"id":"2308.16718","title":"Robust Representation Learning for Unreliable Partial Label Learning","created":"2023-08-31","cited_by_count":2,"area":null},{"id":"2403.16469","title":"Learning from Reduced Labels for Long-Tailed Data","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2407.10000","title":"Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategies","created":"2024-07-13","cited_by_count":2,"area":null},{"id":"2411.10364","title":"Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions","created":"2024-11-15","cited_by_count":2,"area":null},{"id":"2502.07661","title":"Partial-Label Learning with Conformal Candidate Cleaning","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2506.05027","title":"Tuning the Right Foundation Models is What you Need for Partial Label Learning","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2108.06810","title":"SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images","created":"2021-08-15","cited_by_count":1,"area":null},{"id":"2208.09931","title":"ProPaLL: Probabilistic Partial Label Learning","created":"2022-08-21","cited_by_count":1,"area":null},{"id":"2302.12987","title":"Complementary to Multiple Labels: A Correlation-Aware Correction Approach","created":"2023-02-25","cited_by_count":1,"area":null},{"id":"2307.07413","title":"Exploiting Counter-Examples for Active Learning with Partial labels","created":"2023-07-14","cited_by_count":1,"area":null},{"id":"2308.01184","title":"Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation","created":"2023-08-02","cited_by_count":1,"area":null},{"id":"2312.11034","title":"Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning","created":"2023-12-18","cited_by_count":1,"area":null},{"id":"2402.04835","title":"Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning","created":"2024-02-07","cited_by_count":1,"area":null},{"id":"2403.07603","title":"ProPML: Probability Partial Multi-label Learning","created":"2024-03-12","cited_by_count":1,"area":null},{"id":"2410.14894","title":"Soft-Label Integration for Robust Toxicity Classification","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2502.11743","title":"Robust Partial-Label Learning by Leveraging Class Activation Values","created":"2025-02-17","cited_by_count":1,"area":null},{"id":"2505.22028","title":"Weakly-Supervised Contrastive Learning for Imprecise Class Labels","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2510.20925","title":"Learning from Interval Targets","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2510.21934","title":"Joint Score-Threshold Optimization for Interpretable Risk Assessment","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2605.05057","title":"ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2204.03845","title":"Decompositional Generation Process for Instance-Dependent Partial Label Learning","created":"2022-04-08","cited_by_count":0,"area":null},{"id":"2208.12459","title":"Meta Objective Guided Disambiguation for Partial Label Learning","created":"2022-08-26","cited_by_count":0,"area":null},{"id":"2209.09397","title":"Partial sequence labeling with structured Gaussian Processes","created":"2022-09-20","cited_by_count":0,"area":null},{"id":"2302.00299","title":"Learning from Stochastic Labels","created":"2023-02-01","cited_by_count":0,"area":null},{"id":"2303.03946","title":"Pseudo Labels Regularization for Imbalanced Partial-Label Learning","created":"2023-03-06","cited_by_count":0,"area":null},{"id":"2304.00498","title":"Adversary-Aware Partial label learning with Label distillation","created":"2023-04-02","cited_by_count":0,"area":null},{"id":"2307.00553","title":"Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples","created":"2023-07-02","cited_by_count":0,"area":null},{"id":"2407.15036","title":"AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning","created":"2024-07-21","cited_by_count":0,"area":null},{"id":"2408.14788","title":"Learning from Complementary Features","created":"2024-08-27","cited_by_count":0,"area":null},{"id":"2409.19600","title":"An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2410.20797","title":"Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2412.14561","title":"GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning","created":"2024-12-19","cited_by_count":0,"area":null},{"id":"2501.13584","title":"Towards Robust Incremental Learning under Ambiguous Supervision","created":"2025-01-23","cited_by_count":0,"area":null},{"id":"2503.18509","title":"Neuro-symbolic Weak Supervision: Theory and Semantics","created":"2025-03-24","cited_by_count":0,"area":null},{"id":"2505.09354","title":"Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2506.03229","title":"Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2507.00411","title":"Diffusion Disambiguation Models for Partial Label Learning","created":"2025-07-01","cited_by_count":0,"area":"diffusion"},{"id":"2509.11984","title":"Learning from Uncertain Similarity and Unlabeled Data","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2510.03016","title":"Learning Robust Diffusion Models from Imprecise Supervision","created":"2025-10-03","cited_by_count":0,"area":"diffusion"},{"id":"2510.18406","title":"Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2510.21300","title":"Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2511.22823","title":"A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2512.11095","title":"Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.12795","title":"Combating Noisy Labels through Fostering Self- and Neighbor-Consistency","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2603.04825","title":"Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.15781","title":"Learnability with Partial Labels and Adaptive Nearest Neighbors","created":"2026-03-16","cited_by_count":0,"area":null},{"id":"2603.21153","title":"Learning from Label Proportions with Dual-proportion Constraints","created":"2026-03-22","cited_by_count":0,"area":null},{"id":"2604.06614","title":"Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels","created":"2026-04-08","cited_by_count":0,"area":"mllm"},{"id":"2604.22546","title":"ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2605.07413","title":"Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.15586","title":"Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2607.02601","title":"CV-DCLR: Causal-Visual Dynamic Label Refinement for Robust Zero-Shot Learning","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.01281","title":"Latent Softmax for Data-Efficient Phoneme-Based Multilingual ASR Across Tonal and Non-Tonal Languages","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.06896","title":"Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.09236","title":"Label Granularity Skew in Federated Learning with Hierarchical Image Classification","created":"2026-08-10","cited_by_count":0,"area":null}],"next_cursor":"100"}