Least-Ambiguous Multi-Label Classifier

• URL: http://arxiv.org/abs/2509.10689v1
• Date: Fri, 12 Sep 2025 20:45:24 GMT
• Title: Least-Ambiguous Multi-Label Classifier
• Authors: Misgina Tsighe Hagos, Claes Lundström,
• Abstract summary: We propose a model-agnostic approach to single-positive multi-label learning (SPMLL)<n>Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions.
• Score: 2.0482700732041397
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability.

Related papers

• Rethinking Consistent Multi-Label Classification under Inexact Supervision [60.79309683889278]
  In partial multi-label learning, each instance is annotated with a candidate label set, among which only some labels are relevant.<n>In complementary multi-label learning, each instance is annotated with complementary labels indicating the classes to which the instance does not belong.
  arXiv  Detail & Related papers  (2025-10-05T08:30:32Z)
• Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation [120.97262070068224]
  Multi-label learning (MLL) has gained attention for its ability to represent real-world data.<n>Label Distribution Learning (LDL) faces challenges in collecting accurate label distributions.
  arXiv  Detail & Related papers  (2025-02-03T09:04:03Z)
• Determined Multi-Label Learning via Similarity-Based Prompt [12.428779617221366]
  In multi-label classification, each training instance is associated with multiple class labels simultaneously. To alleviate this problem, a novel labeling setting termed textitDetermined Multi-Label Learning (DMLL) is proposed.
  arXiv  Detail & Related papers  (2024-03-25T07:08:01Z)
• Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels [63.16824565919966]
  This paper proposes to use confusing samples proactively without label correction. A Virtual Category (VC) is assigned to each confusing sample in such a way that it can safely contribute to the model optimisation. Our intriguing findings highlight the usage of VC learning in dense vision tasks.
  arXiv  Detail & Related papers  (2023-12-02T16:23:52Z)
• Understanding Label Bias in Single Positive Multi-Label Learning [20.09309971112425]
  It is possible to train effective multi-labels using only one positive label per image. Standard benchmarks for SPML are derived from traditional multi-label classification datasets. This work introduces protocols for studying label bias in SPML and provides new empirical results.
  arXiv  Detail & Related papers  (2023-05-24T21:41:08Z)
• Deep Partial Multi-Label Learning with Graph Disambiguation [27.908565535292723]
  We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN) Specifically, we introduce the instance-level and label-level similarities to recover label confidences. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
  arXiv  Detail & Related papers  (2023-05-10T04:02:08Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Acknowledging the Unknown for Multi-label Learning with Single Positive Labels [65.5889334964149]
  Traditionally, all unannotated labels are assumed as negative labels in single positive multi-label learning (SPML) We propose entropy-maximization (EM) loss to maximize the entropy of predicted probabilities for all unannotated labels. Considering the positive-negative label imbalance of unannotated labels, we propose asymmetric pseudo-labeling (APL) with asymmetric-tolerance strategies and a self-paced procedure to provide more precise supervision.
  arXiv  Detail & Related papers  (2022-03-30T11:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.