Active Learning and Transfer Learning for Document Segmentation

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Programming and Computer Software Pub Date : 2023-12-07 DOI:10.1134/s0361768823070046

D. M. Kiranov, M. A. Ryndin, I. S. Kozlov

引用次数: 1

Abstract

In this paper, we investigate the effectiveness of classical approaches to active learning in the problem of document segmentation with the aim of reducing the size of the training sample. A modified approach to selection of document images for labeling and subsequent model training is presented. The results of active learning are compared to those of transfer learning on fully labeled data. The paper also investigates how the problem domain of a training set, on which a model is initialized for transfer learning, affects the subsequent uptraining of the model.

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文档分割的主动学习和迁移学习

摘要在本文中，我们研究了主动学习的经典方法在文档分割问题中的有效性，目的是减少训练样本的大小。本文介绍了一种改进的方法，用于选择文档图像进行标记和随后的模型训练。将主动学习的结果与在完全标记数据上进行迁移学习的结果进行了比较。论文还研究了用于迁移学习的模型初始化的训练集的问题域如何影响模型的后续向上训练。

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

Programming and Computer Software 工程技术-计算机：软件工程

CiteScore

1.60

自引率

28.60%

发文量

审稿时长

>12 weeks

期刊介绍： Programming and Computer Software is a peer reviewed journal devoted to problems in all areas of computer science: operating systems, compiler technology, software engineering, artificial intelligence, etc.