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CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning

Authors:

Dingcheng LiAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2121 - 2125

https://doi.org/10.1145/3404835.3463096

Published: 11 July 2021 Publication History

Abstract

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and attributes. In this paper, we consider the following challenging multi-label stream scenario: the new labels emerge continuously in the changing environments, and are assigned to the previous data. In this setting, data mining solutions must be able to learn the new concepts and avoid catastrophic forgetting simultaneously. We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels. We utilize the knowledge from the previous tasks to learn new knowledge to solve the current task. Then, the system compresses historical and novel knowledge and preserves it while waiting for new emerging tasks. CIFDM consists of three components: 1) a knowledge bank that stores the existing feature-level compressed knowledge, and predicts the observed labels so far; 2) a pioneer module that aims to learn and predict new emerged labels based on knowledge bank.; 3) an interactive knowledge compression function which is used to compress and transfer the new knowledge to the bank, and then apply the current compressed knowledge to initialize the label embedding of the pioneer for the next task.

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Cited By

Gaudreault JBranco P(2024)A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and OpportunitiesACM Computing Surveys10.1145/365728656:10(1-37)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3657286
Awal Kassim MViktor HMichalowski W(2024)Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and ApplicationsIEEE Access10.1109/ACCESS.2024.340356912(74539-74557)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403569
Wen HZhang XSong XWei YNie LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Target-Guided Composed Image RetrievalProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611817(915-923)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611817
Show More Cited By

Index Terms

CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Gaudreault JBranco P(2024)A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and OpportunitiesACM Computing Surveys10.1145/365728656:10(1-37)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3657286
Awal Kassim MViktor HMichalowski W(2024)Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and ApplicationsIEEE Access10.1109/ACCESS.2024.340356912(74539-74557)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403569
Wen HZhang XSong XWei YNie LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Target-Guided Composed Image RetrievalProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611817(915-923)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611817
Zhang DTaneva-Popova BYoshioka MKiseleva JAliannejadi M(2023)A Theoretical Analysis of Out-of-Distribution Detection in Multi-Label ClassificationProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605116(275-282)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605116
Li DYang PZhang YLi PChen HDuh WHuang HKato MMothe JPoblete B(2023)Power Norm Based Lifelong Learning for Paraphrase GenerationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592039(2266-2271)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592039
Mishra SSundaram S(2023)A Memory-Free Evolving Bipolar Neural Network for Efficient Multi-Label Stream LearningICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095002(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10095002
Costa JFaria ESilva JGama JCerri R(2023)Novelty detection for multi-label stream classification under extreme verification latencyApplied Soft Computing10.1016/j.asoc.2023.110265141:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110265
Wang ZLi DLi PAl Hasan MXiong L(2022)Latent Coreset Sampling based Data-Free Continual LearningProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557375(2077-2087)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557375

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