Provably Consistent Partial-Label Learning

• URL: http://arxiv.org/abs/2007.08929v2
• Date: Fri, 23 Oct 2020 11:22:28 GMT
• Title: Provably Consistent Partial-Label Learning
• Authors: Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
• Abstract summary: Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. In this paper, we propose the first generation model of candidate label sets, and develop two novel methods that are guaranteed to be consistent. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two methods.
• Score: 120.4734093544867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two novel PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.

Related papers

• Realistic Evaluation of Deep Partial-Label Learning Algorithms [94.79036193414058]
  Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent years, many deep algorithms have been developed to improve model performance. Some early developed algorithms are often underestimated and can outperform many later algorithms with complicated designs.
  arXiv  Detail & Related papers  (2025-02-14T14:22:16Z)
• Partial-Label Learning with Conformal Candidate Cleaning [0.0]
  This article proposes a novel enhancing method that incrementally prunes candidate sets using conformal prediction. We establish that our pruning method preserves the conformal validity with respect to the unknown ground truth.
  arXiv  Detail & Related papers  (2025-02-11T15:51:23Z)
• 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)
• Complementary Classifier Induced Partial Label Learning [54.61668156386079]
  In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional classifier.
  arXiv  Detail & Related papers  (2023-05-17T02:13:23Z)
• Meta Objective Guided Disambiguation for Partial Label Learning [44.05801303440139]
  We propose a novel framework for partial label learning with meta objective guided disambiguation (MoGD) MoGD aims to recover the ground-truth label from candidate labels set by solving a meta objective on a small validation set. The proposed method can be easily implemented by using various deep networks with the ordinary SGD.
  arXiv  Detail & Related papers  (2022-08-26T06:48:01Z)
• Progressive Purification for Instance-Dependent Partial Label Learning [37.65717805892473]
  Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. The candidate labels are always instance-dependent in practice and there is no theoretical guarantee that the model trained on the instance-dependent examples can converge to an ideal one. In this paper, a theoretically grounded and practically effective approach named POP, i.e. PrOgressive Purification, is proposed. Specifically, POP updates the learning model and purifies each candidate label set progressively in every epoch.
  arXiv  Detail & Related papers  (2022-06-02T02:07:12Z)
• Decomposition-based Generation Process for Instance-Dependent Partial Label Learning [45.133781119468836]
  Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing 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. We propose Maximum A Posterior(MAP) based on an explicitly modeled generation process of candidate labels.
  arXiv  Detail & Related papers  (2022-04-08T05:18:51Z)
• Progressive Identification of True Labels for Partial-Label Learning [112.94467491335611]
  Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. This paper proposes a novel framework of classifier with flexibility on the model and optimization algorithm.
  arXiv  Detail & Related papers  (2020-02-19T08:35:15Z)

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.