{"title":"Inductive State-Relabeling Adversarial Active Learning with Heuristic Clique Rescaling.","authors":"Beichen Zhang, Liang Li, Shuhui Wang, Shaofei Cai, Zheng-Jun Zha, Qi Tian, Qingming Huang","doi":"10.1109/TPAMI.2024.3432099","DOIUrl":null,"url":null,"abstract":"<p><p>Active learning (AL) is to design label-efficient algorithms by labeling the most representative samples. It reduces annotation cost and attracts increasing attention from the community. However, previous AL methods suffer from the inadequacy of annotations and unreliable uncertainty estimation. Moreover, we find that they ignore the intra-diversity of selected samples, which leads to sampling redundancy. In view of these challenges, we propose an inductive state-relabeling adversarial AL model (ISRA) that consists of a unified representation generator, an inductive state-relabeling discriminator, and a heuristic clique rescaling module. The generator introduces contrastive learning to leverage unlabeled samples for self-supervised training, where the mutual information is utilized to improve the representation quality for AL selection. Then, we design an inductive uncertainty indicator to learn the state score from labeled data and relabel unlabeled data with different importance for better discrimination of instructive samples. To solve the problem of sampling redundancy, the heuristic clique rescaling module measures the intra-diversity of candidate samples and recurrently rescales them to select the most informative samples. The experiments conducted on eight datasets and two imbalanced scenarios show that our model outperforms the previous state-of-the-art AL methods. As an extension on the cross-modal AL task, we apply ISRA to the image captioning and it also achieves superior performance.</p>","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on pattern analysis and machine intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TPAMI.2024.3432099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Active learning (AL) is to design label-efficient algorithms by labeling the most representative samples. It reduces annotation cost and attracts increasing attention from the community. However, previous AL methods suffer from the inadequacy of annotations and unreliable uncertainty estimation. Moreover, we find that they ignore the intra-diversity of selected samples, which leads to sampling redundancy. In view of these challenges, we propose an inductive state-relabeling adversarial AL model (ISRA) that consists of a unified representation generator, an inductive state-relabeling discriminator, and a heuristic clique rescaling module. The generator introduces contrastive learning to leverage unlabeled samples for self-supervised training, where the mutual information is utilized to improve the representation quality for AL selection. Then, we design an inductive uncertainty indicator to learn the state score from labeled data and relabel unlabeled data with different importance for better discrimination of instructive samples. To solve the problem of sampling redundancy, the heuristic clique rescaling module measures the intra-diversity of candidate samples and recurrently rescales them to select the most informative samples. The experiments conducted on eight datasets and two imbalanced scenarios show that our model outperforms the previous state-of-the-art AL methods. As an extension on the cross-modal AL task, we apply ISRA to the image captioning and it also achieves superior performance.
主动学习(AL)是通过标注最具代表性的样本来设计标签效率高的算法。它降低了标注成本,受到越来越多的关注。然而,以往的主动学习方法存在注释不足和不确定性估计不可靠的问题。此外,我们还发现这些方法忽略了所选样本的内部多样性,从而导致了采样冗余。鉴于这些挑战,我们提出了一种归纳式状态标注对抗 AL 模型(ISRA),它由一个统一的表示生成器、一个归纳式状态标注判别器和一个启发式聚类重缩模块组成。表示生成器引入了对比学习,利用未标记样本进行自我监督训练,利用互信息来提高 AL 选择的表示质量。然后,我们设计了一个归纳式不确定性指标,从已标注数据中学习状态得分,并对未标注数据进行不同重要性的重新标注,以更好地辨别指导性样本。为解决采样冗余问题,启发式聚类重定标模块会测量候选样本的内部多样性,并对其进行循环重定标,以选择信息量最大的样本。在八个数据集和两个不平衡场景下进行的实验表明,我们的模型优于之前最先进的 AL 方法。作为跨模态 AL 任务的扩展,我们将 ISRA 应用于图像字幕,同样取得了优异的性能。