Active self-semi-supervised learning for few labeled samples

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ziting Wen , Oscar Pizarro , Stefan Williams
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引用次数: 0

Abstract

Training deep models with limited annotations poses a significant challenge when applied to diverse practical domains. Employing semi-supervised learning alongside the self-supervised model offers the potential to enhance label efficiency. However, this approach faces a bottleneck in reducing the need for labels. We observed that the semi-supervised model disrupts valuable information from self-supervised learning when only limited labels are available. To address this issue, this paper proposes a simple yet effective framework, active self-semi-supervised learning (AS3L). AS3L bootstraps semi-supervised models with prior pseudo-labels (PPL). These PPLs are obtained by label propagation over self-supervised features. Based on the observations the accuracy of PPL is not only affected by the quality of features but also by the selection of the labeled samples. We develop active learning and label propagation strategies to obtain accurate PPL. Consequently, our framework can significantly improve the performance of models in the case of limited annotations while demonstrating fast convergence. On the image classification tasks across four datasets, our method outperforms the baseline by an average of 5.4%. Additionally, it achieves the same accuracy as the baseline method in about 1/3 of the training time.
针对少量标注样本的主动半监督学习
在应用于各种实际领域时,利用有限的注释来训练深度模型是一项重大挑战。在自监督模型的同时采用半监督学习,有可能提高标签效率。然而,这种方法在减少标签需求方面面临瓶颈。我们发现,当只有有限的标签时,半监督模型会破坏自监督学习的宝贵信息。为了解决这个问题,本文提出了一个简单而有效的框架--主动自半监督学习(AS3L)。AS3L 利用先验伪标签 (PPL) 引导半监督模型。这些 PPL 是通过自监督特征的标签传播获得的。根据观察,PPL 的准确性不仅受特征质量的影响,还受标签样本选择的影响。我们开发了主动学习和标签传播策略,以获得准确的 PPL。因此,我们的框架可以在注释有限的情况下显著提高模型的性能,同时表现出快速收敛性。在四个数据集的图像分类任务中,我们的方法平均比基线方法高出 5.4%。此外,它还能在大约 1/3 的训练时间内达到与基准方法相同的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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