Learning from Concealed Labels

• URL: http://arxiv.org/abs/2412.02230v1
• Date: Tue, 03 Dec 2024 08:00:19 GMT
• Title: Learning from Concealed Labels
• Authors: Zhongnian Li, Meng Wei, Peng Ying, Tongfeng Sun, Xinzheng Xu,
• Abstract summary: We propose a novel setting to protect privacy of each instance, namely learning from concealed labels for multi-class classification. Concealed labels prevent sensitive labels from appearing in the label set during the label collection stage, which specifies none and some random sampled insensitive labels as concealed labels set to annotate sensitive data.
• Score: 5.235218636685312
• License:
• Abstract: Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a novel setting to protect privacy of each instance, namely learning from concealed labels for multi-class classification. Concealed labels prevent sensitive labels from appearing in the label set during the label collection stage, which specifies none and some random sampled insensitive labels as concealed labels set to annotate sensitive data. In this paper, an unbiased estimator can be established from concealed data under mild assumptions, and the learned multi-class classifier can not only classify the instance from insensitive labels accurately but also recognize the instance from the sensitive labels. Moreover, we bound the estimation error and show that the multi-class classifier achieves the optimal parametric convergence rate. Experiments demonstrate the significance and effectiveness of the proposed method for concealed labels in synthetic and real-world datasets.

Related papers

• Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning [53.64180787439527]
  In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. For the first time, we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels. To reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information.
  arXiv  Detail & Related papers  (2024-12-06T13:25:39Z)
• You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling [60.27812493442062]
  We show the importance of investigating labeled data quality to improve any pseudo-labeling method. Specifically, we introduce a novel data characterization and selection framework called DIPS to extend pseudo-labeling. We demonstrate the applicability and impact of DIPS for various pseudo-labeling methods across an extensive range of real-world datasets.
  arXiv  Detail & Related papers  (2024-06-19T17:58:40Z)
• Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels [7.396461226948109]
  We address the limitations of the common data annotation and training methods for objective single-label classification tasks. Our findings indicate that additional annotator information, such as confidence, secondary label and disagreement, can be used to effectively generate soft labels.
  arXiv  Detail & Related papers  (2023-11-09T10:47:39Z)
• Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels [8.977819892091]
  Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In this work, we address the issue of label imbalance and investigate how to train neural networks using partial labels.
  arXiv  Detail & Related papers  (2023-07-31T21:50:48Z)
• Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations [91.67511167969934]
  imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
  arXiv  Detail & Related papers  (2023-05-22T04:50:28Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• 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)
• A Study on the Autoregressive and non-Autoregressive Multi-label Learning [77.11075863067131]
  We propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies.
  arXiv  Detail & Related papers  (2020-12-03T05:41:44Z)

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.