基于最大影响力集合的主动学习方法

doi:10.11896/jsjkx.231100075

Abstract

Abstract: With the continuous progress of deep learning,it has been widely applied in numerous fields.However,the training of deep models requires a large amount of labeled data,and the cost of time and resources is high.How to maximize the model performance with the least amount of labeled data has become an important research topic.Active learning aims to address this issue by selecting the most valuable samples for annotation and utilizing them for model training.Traditional active learning approaches usually concentrate on uncertainty or diversity,aiming to query the most difficult or representative samples.Nevertheless,these methods typically only take into account one-sided effects and overlook the interaction between labeled and unlabeled data in active learning scenarios.Another type of active learning method utilizes auxiliary networks for sample selection,but these methods usually result in higher computational complexity.This paper proposes a novel active learning approach designed to optimize the model’s total performance gain by taking into account sample-to-sample interactions and comprehensively measuring local uncertainty and the influence of candidate samples on other samples.The method first estimates the influence of samples on each other based on the distance between the hidden layer representations of the samples,and further estimates the potential gain that the sample can bring based on the influence of candidate samples and the uncertainty of unlabeled samples.The sample with the highest global gain is iteratively chosen for annotation.On a series of tasks across several domains,the study further compares the proposed method with other active learning strategies.Experimental results demonstrate that the proposed method outperforms all competitors in all tasks.Further quantitative analysis experiments have also demonstrated that it balances uncertainty and diversity well,and explores the factors that should be emphasized at different stages of active learning.

Key words: Active learning, Deep learning, Uncertainty

CLC Number:

TP391

LI Yahe, XIE Zhipeng. Active Learning Based on Maximum Influence Set[J].Computer Science, 2025, 52(1): 289-297.

References

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