Complementary Labels Learning with Augmented Classes

• URL: http://arxiv.org/abs/2211.10701v1
• Date: Sat, 19 Nov 2022 13:55:27 GMT
• Title: Complementary Labels Learning with Augmented Classes
• Authors: Zhongnian Li, Jian Zhang, Mengting Xu, Xinzheng Xu, Daoqiang Zhang
• Abstract summary: Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning. We propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC) By using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent.
• Score: 22.460256396941528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing phase. In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method.

Related papers

• An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes [46.663081214928226]
  We propose an unbiased risk estimator with theoretical guarantees for PLLAC. We provide a theoretical analysis of the estimation error bound of PLLAC. Experiments on benchmark, UCI and real-world datasets demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-09-29T07:36:16Z)
• AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning [5.0823084858349485]
  We present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data. The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-22T06:59:52Z)
• 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)
• 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)
• Class-Imbalanced Complementary-Label Learning via Weighted Loss [8.934943507699131]
  Complementary-label learning (CLL) is widely used in weakly supervised classification. It faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. We propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification.
  arXiv  Detail & Related papers  (2022-09-28T16:02:42Z)
• Reduction from Complementary-Label Learning to Probability Estimates [15.835526669091157]
  Complementary-Label Learning (CLL) is a weakly-supervised learning problem. This paper introduces a novel perspective--reduction to probability estimates of complementary classes. It offers explanations of several key CLL approaches and allows us to design an improved algorithm.
  arXiv  Detail & Related papers  (2022-09-20T06:36:51Z)
• Continual Learning For On-Device Environmental Sound Classification [63.81276321857279]
  We propose a simple and efficient continual learning method for on-device environmental sound classification. Our method selects the historical data for the training by measuring the per-sample classification uncertainty.
  arXiv  Detail & Related papers  (2022-07-15T12:13:04Z)
• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning [53.1047775185362]
  Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process.
  arXiv  Detail & Related papers  (2021-01-15T23:29:57Z)
• 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.