research-article

Detecting the Fake Candidate Instances: Ambiguous Label Learning with Generative Adversarial Networks

Authors:

Yiming WangAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 903 - 912

https://doi.org/10.1145/3459637.3482251

Published: 30 October 2021 Publication History

Abstract

Ambiguous Label Learning (ALL), as an emerging paradigm of weakly supervised learning, aims to induce the prediction model from training datasets with ambiguous supervision, where, specifically, each training instance is annotated with a set of candidate labels but only one is valid. To handle this task, the existing shallow methods mainly disambiguate the candidate labels by leveraging various regularization techniques. Inspired by the great success of deep generative adversarial networks, we apply it to perform effective candidate label disambiguation from a new instance-pivoted perspective. Specifically, for each ALL instance, we recombine its feature representation with each of candidate labels to generate a set of candidate instances, where only one is real and all others are fake. We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. The discriminator is used to detect the fake candidate instances, so that the classifier can be trained without them. With this insight, we develop a novel ALL method, namely Adversarial Ambiguous Label Learning with Candidate Instance Detection (A2L2CID). Theoretically, we analyze that there is a global equilibrium point between the three players. Empirically, extensive experimental results indicate that A2L2CID outperforms the state-of-the-art ALL methods.

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https://doi.org/10.1016/j.neucom.2025.129606
Qian WLi YYe QXia SHuang JDing W(2024)Confidence-Induced Granular Partial Label Feature Selection via Dependency and SimilarityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340548936:11(5797-5810)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3405489
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https://doi.org/10.1016/j.patcog.2024.110791
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Index Terms

Detecting the Fake Candidate Instances: Ambiguous Label Learning with Generative Adversarial Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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National Natural Science Foundation of China (NSFC)
Key R&D Projects of Science and Technology Department of Jilin Province of China

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Liu ZZhang ZLu HWang W(2025)Addressing bayes imbalance in partial label learning via range adaptive graph guided disambiguationNeurocomputing10.1016/j.neucom.2025.129606627(129606)Online publication date: Apr-2025
https://doi.org/10.1016/j.neucom.2025.129606
Qian WLi YYe QXia SHuang JDing W(2024)Confidence-Induced Granular Partial Label Feature Selection via Dependency and SimilarityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340548936:11(5797-5810)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3405489
Qian WLiu JYang WHuang JDing W(2024)Partial label feature selection based on noisy manifold and label distributionPattern Recognition10.1016/j.patcog.2024.110791156(110791)Online publication date: Dec-2024
https://doi.org/10.1016/j.patcog.2024.110791
Wang HXia MLi YMao YFeng LChen GZhao JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)SoLarProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600858(8104-8117)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600858
Wang YGuan YWang BLi X(2022)Learning with partial multi-labeled data by leveraging low-rank constraint and decompositionApplied Intelligence10.1007/s10489-022-03989-053:7(8133-8145)Online publication date: 28-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03989-0

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