Partial Label Learning

Benchmarks archive 2025-07-28

11 leaderboard tables shown for this task, 11 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 11 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 38 papers with code (85 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Exploiting Class Activation Value for Partial-Label Learning
  29 Sep 2021 3 repositories listed
  As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate…
• Tuning the Right Foundation Models is What you Need for Partial Label Learning
  5 Jun 2025 1 repository listed
  Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications.
• Partial Label Clustering
  6 May 2025 1 repository listed
  Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels and only one label is the ground-truth label.
• Multi-Instance Partial-Label Learning with Margin Adjustment
  22 Jan 2025 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set.
• Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
  6 Dec 2024 1 repository listed
  In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels.
• Training a Label-Noise-Resistant GNN with Reduced Complexity
  17 Nov 2024 1 repository listed
  This mitigates the impact of inaccurately labeled neighbors and diversifies the label set.
• An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
  29 Sep 2024 1 repository listed
  To address this issue, in this paper, we focus on the problem of Partial Label Learning with Augmented Class (PLLAC), where one or more augmented classes are not visible in the training stage but appear in the inference…
• Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
  26 Aug 2024 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space.
• AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
  21 Jul 2024 1 repository listed
  Specifically, the disambiguation network is trained with self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise…
• Partial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts
  5 Jun 2024 1 repository listed
  In this paper, we present a novel GeoAI approach to training sea ice classification by formalizing it as a partial label learning task with explicit confidence scores to address multiple labels and class imbalance.
• Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification
  27 May 2024 1 repository listed
  In this paper, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.
• Partial-Label Learning with a Reject Option
  1 Feb 2024 1 repository listed
  In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels.
• Partial Label Supervision for Agnostic Generative Noisy Label Learning
  2 Aug 2023 1 repository listed
  Second, we introduce a new Partial Label Supervision (PLS) for noisy label learning that accounts for both clean label coverage and uncertainty.
• Partial-Label Regression
  15 Jun 2023 1 repository listed
  Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses.
• Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification
  25 May 2023 1 repository listed
  In this letter, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.
• S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
  23 May 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures,…
• Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
  22 May 2023 1 repository listed
  In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations.
• A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
  21 May 2023 1 repository listed
  Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
• Complementary Classifier Induced Partial Label Learning
  17 May 2023 1 repository listed
  complementary labels), which accurately indicates a set of labels that do not belong to a sample.
• Towards Effective Visual Representations for Partial-Label Learning
  10 May 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance…
• Partial Label Learning for Emotion Recognition from EEG
  25 Feb 2023 1 repository listed
  We also investigate the effect of label disambiguation, a key step in many PLL methods.
• Long-Tailed Partial Label Learning via Dynamic Rebalancing
  10 Feb 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  The straightforward combination of LT and PLL, i.
• Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision
  18 Dec 2022 1 repository listed
  MIPLGP first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an…
• Learning with Partial Labels from Semi-supervised Perspective
  24 Nov 2022 1 repository listed
  Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with…
• IRNet: Iterative Refinement Network for Noisy Partial Label Learning
  9 Nov 2022 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set.
• SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
  21 Sep 2022 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth.
• CASS: Cross Architectural Self-Supervision for Medical Image Analysis
  8 Jun 2022 1 repository listed
  We also show that CASS is much more robust to changes in batch size and training epochs.
• Decompositional Generation Process for Instance-Dependent Partial Label Learning
  8 Apr 2022 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way.
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
  11 Feb 2022 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting…
• Webly-Supervised Fine-Grained Recognition with Partial Label Learning
  9 Feb 2022 1 repository listed
  The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.