{"total":21,"rows":[{"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":"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":"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":"2501.12597","title":"Multi-Instance Partial-Label Learning with Margin Adjustment","created":"2025-01-22","cited_by_count":5,"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":"2402.00592","title":"Partial-Label Learning with a Reject Option","created":"2024-02-01","cited_by_count":3,"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":"2506.11599","title":"A$^2$LC: Active and Automated Label Correction for Semantic Segmentation","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2309.13886","title":"Can Class-Priors Help Single-Positive Multi-Label Learning?","created":"2023-09-25","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":"2512.17788","title":"Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning","created":"2025-12-19","cited_by_count":1,"area":null},{"id":"2408.14788","title":"Learning from Complementary Features","created":"2024-08-27","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":"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":"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":"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":"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}],"next_cursor":null}