Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning

• URL: http://arxiv.org/abs/2505.09354v1
• Date: Wed, 14 May 2025 13:04:55 GMT
• Title: Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
• Authors: Guangtai Wang, Chi-Man Vong, Jintao Huang,
• Abstract summary: We show that clean samples can be collected to offer guidance and enhance the confidence of the most possible candidates.<n>We attribute the most reliable candidates with higher significance under the assumption that for each clean sample, if its label is one of the candidates of its nearest neighbor in the representation space, it is more likely to be the ground truth of its neighbor.
• Score: 8.969478423832188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diminishing the impact of false-positive labels is critical for conducting disambiguation in partial label learning. However, the existing disambiguation strategies mainly focus on exploiting the characteristics of individual partial label instances while neglecting the strong supervision information of clean samples randomly lying in the datasets. In this work, we show that clean samples can be collected to offer guidance and enhance the confidence of the most possible candidates. Motivated by the manner of the differentiable count loss strat- egy and the K-Nearest-Neighbor algorithm, we proposed a new calibration strategy called CleanSE. Specifically, we attribute the most reliable candidates with higher significance under the assumption that for each clean sample, if its label is one of the candidates of its nearest neighbor in the representation space, it is more likely to be the ground truth of its neighbor. Moreover, clean samples offer help in characterizing the sample distributions by restricting the label counts of each label to a specific interval. Extensive experiments on 3 synthetic benchmarks and 5 real-world PLL datasets showed this calibration strategy can be applied to most of the state-of-the-art PLL methods as well as enhance their performance.

Related papers

• Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data [18.111971239860836]
  We propose a novel sample selection method for image classification in the presence of noisy labels.<n>Our goal is to accurately distinguish correctly labeled yet hard-to-learn samples from mislabeled ones.<n>Our method functions as a plug-and-play component that can be seamlessly integrated into existing sample selection techniques.
  arXiv  Detail & Related papers  (2025-04-24T12:07:14Z)
• Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
  We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching. By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously. Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
  arXiv  Detail & Related papers  (2024-04-10T07:34:37Z)
• Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning [8.387189407144403]
  We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive.<n>We focus on Partial Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels.<n>We present a framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm.
  arXiv  Detail & Related papers  (2024-02-07T13:32:47Z)
• Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning [55.4510979153023]
  In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. To help these mislabeled samples "appeal," we propose the first appeal-based framework.
  arXiv  Detail & Related papers  (2023-12-18T09:09:52Z)
• 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)
• Combating Label Noise With A General Surrogate Model For Sample Selection [77.45468386115306]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically.<n>We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• PASS: Peer-Agreement based Sample Selection for training with Noisy Labels [16.283722126438125]
  The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples. We propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem.
  arXiv  Detail & Related papers  (2023-03-20T00:35:33Z)
• Neighborhood Collective Estimation for Noisy Label Identification and Correction [92.20697827784426]
  Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
  arXiv  Detail & Related papers  (2022-08-05T14:47:22Z)
• Sample Prior Guided Robust Model Learning to Suppress Noisy Labels [8.119439844514973]
  We propose PGDF, a novel framework to learn a deep model to suppress noise by generating the samples' prior knowledge. Our framework can save more informative hard clean samples into the cleanly labeled set. We evaluate our method using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world datasets WebVision and Clothing1M.
  arXiv  Detail & Related papers  (2021-12-02T13:09:12Z)
• Instance-Dependent Partial Label Learning [69.49681837908511]
  Partial label learning is a typical weakly supervised learning problem. Most existing approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. In this paper, we consider instance-dependent and assume that each example is associated with a latent label distribution constituted by the real number of each label.
  arXiv  Detail & Related papers  (2021-10-25T12:50:26Z)
• An Empirical Investigation of Learning from Biased Toxicity Labels [15.822714574671412]
  We study how different training strategies can leverage a small dataset of human-annotated labels and a large but noisy dataset of synthetically generated labels. We evaluate the accuracy and fairness properties of these approaches, and trade-offs between the two.
  arXiv  Detail & Related papers  (2021-10-04T17:19:57Z)

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.