Learning from Uncertain Similarity and Unlabeled Data

• URL: http://arxiv.org/abs/2509.11984v1
• Date: Mon, 15 Sep 2025 14:29:36 GMT
• Title: Learning from Uncertain Similarity and Unlabeled Data
• Authors: Meng Wei, Zhongnian Li, Peng Ying, Xinzheng Xu,
• Abstract summary: We propose Uncertain Similarity and Unlabeled Learning (USimUL), a novel framework where each similarity pair is embedded with an uncertainty component to reduce label leakage.<n>We show that our method achieves superior classification performance compared to conventional similarity-based approaches.
• Score: 6.553242735096595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing similarity-based weakly supervised learning approaches often rely on precise similarity annotations between data pairs, which may inadvertently expose sensitive label information and raise privacy risks. To mitigate this issue, we propose Uncertain Similarity and Unlabeled Learning (USimUL), a novel framework where each similarity pair is embedded with an uncertainty component to reduce label leakage. In this paper, we propose an unbiased risk estimator that learns from uncertain similarity and unlabeled data. Additionally, we theoretically prove that the estimator achieves statistically optimal parametric convergence rates. Extensive experiments on both benchmark and real-world datasets show that our method achieves superior classification performance compared to conventional similarity-based approaches.

Related papers

• Semi-Supervised Regression with Heteroscedastic Pseudo-Labels [50.54050677867914]
  We propose an uncertainty-aware pseudo-labeling framework that dynamically adjusts pseudo-label influence from a bi-level optimization perspective.<n>We provide theoretical insights and extensive experiments to validate our approach across various benchmark SSR datasets.
  arXiv  Detail & Related papers  (2025-10-17T03:06:23Z)
• Learning from Similarity-Confidence and Confidence-Difference [0.24578723416255752]
  We propose a novel Weakly Supervised Learning (WSL) framework that leverages complementary weak supervision signals from multiple perspectives.<n>Specifically, we introduce SconfConfDiff Classification, a method that integrates two distinct forms of weaklabels.<n>We prove that both estimators achieve optimal convergence rates with respect to estimation error bounds.
  arXiv  Detail & Related papers  (2025-08-07T07:42:59Z)
• Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification [6.920336485308536]
  Pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification. We develop a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. In order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss.
  arXiv  Detail & Related papers  (2024-07-04T03:04:56Z)
• Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical [66.57396042747706]
  Complementary-label learning is a weakly supervised learning problem. We propose a consistent approach that does not rely on the uniform distribution assumption. We find that complementary-label learning can be expressed as a set of negative-unlabeled binary classification problems.
  arXiv  Detail & Related papers  (2023-11-27T02:59:17Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Learning from Similarity-Confidence Data [94.94650350944377]
  We investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data. We propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate.
  arXiv  Detail & Related papers  (2021-02-13T07:31:16Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Pointwise Binary Classification with Pairwise Confidence Comparisons [97.79518780631457]
  We propose pairwise comparison (Pcomp) classification, where we have only pairs of unlabeled data that we know one is more likely to be positive than the other. We link Pcomp classification to noisy-label learning to develop a progressive URE and improve it by imposing consistency regularization.
  arXiv  Detail & Related papers  (2020-10-05T09:23:58Z)

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.