Review of lifelong learning in computer vision

doi:10.11772/j.issn.1001-9081.2022050766

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1785-1795.DOI: 10.11772/j.issn.1001-9081.2022050766

Special Issue: 人工智能；综述

• Artificial intelligence • Previous Articles Next Articles

Review of lifelong learning in computer vision

Yichi CHEN¹^,², Bin CHEN²^,³^,⁴()

^1.Chengdu Institute of Computer Application，Chinese Academy of Sciences，Chengdu Sichuan 610041，China
^2.School of Computer Science and Technology，University of Chinese Academy of Sciences，Beijing 100049，China
^3.International Institute for Artificial Intelligence，Harbin Institute of Technology （Shenzhen），Shenzhen Guangdong 518055，China
^4.Chongqing Research Institute，Harbin Institute of Technology，Chongqing 401151，China

Received:2022-05-27 Revised:2022-09-27 Accepted:2022-10-13 Online:2023-06-08 Published:2023-06-10
Contact: Bin CHEN
About author:CHEN Yichi， born in 1997， Ph. D. candidate. His research interests include computer vision， lifelong learning.
Supported by:
2022 Key Program of Research and Development of State Administration of Science, Technology and Industry for National Defence(D020401)

计算机视觉中的终身学习综述

陈一驰¹^,², 陈斌²^,³^,⁴()

^1.中国科学院成都计算机应用研究所, 成都 610041
^2.中国科学院大学计算机科学与技术学院, 北京 100049
^3.哈尔滨工业大学(深圳) 国际人工智能研究院, 广东深圳 518055
^4.哈尔滨工业大学重庆研究院, 重庆 401151

通讯作者: 陈斌
作者简介:陈一驰（1997—），男，湖南岳阳人，博士研究生，主要研究方向：计算机视觉、终身学习
陈斌（1970—），男，四川广汉人，研究员，博士，主要研究方向：机器视觉、模式识别、增量学习、工业质检Email：chenbin2020@hit.edu.cn。

Abstract

Abstract:

LifeLong learning （LLL）， as an emerging method， breaks the limitations of traditional machine learning and gives the models the ability to accumulate， optimize and transfer knowledge in the learning process like human beings. In recent years， with the wide application of deep learning， more and more studies attempt to solve catastrophic forgetting problem in deep neural networks and get rid of the stability-plasticity dilemma， as well as apply LLL methods to a wide varieties of real-world scenarios to promote the development of artificial intelligence from weak to strong. Aiming at the field of computer vision， firstly， LLL methods were classified into four types in image classification tasks： data-driven methods， optimization process based methods， network structure based methods and knowledge combination based methods. Then， typical applications of LLL methods in other visual tasks and related evaluation indicators were introduced. Finally， the deficiencies of LLL methods at current stage were discussed， and the future development directions of LLL methods were proposed.

Key words: deep learning, LifeLong Learning (LLL), computer vision, catastrophic forgetting, stability-plasticity dilemma

摘要：

终身学习（LLL）作为一种新兴方法打破了传统机器学习的局限性，并赋予了模型能够像人类一样在学习过程中不断积累、优化并转移知识的能力。近年来，随着深度学习的广泛应用，越来越多的研究致力于解决深度神经网络中出现的灾难性遗忘问题和摆脱稳定性-可塑性困境，并将LLL方法应用于各种各样的实际场景中，以推进人工智能由弱向强的发展。针对计算机视觉领域，首先，在图像分类任务中将LLL方法归纳为四大类型：基于数据驱动的方法、基于优化过程的方法、基于网络结构的方法和基于知识组合的方法；然后，介绍了LLL方法在其他视觉任务中的典型应用和相关评估指标；最后，针对现阶段LLL方法的不足之处进行讨论并提出了LLL方法未来发展的方向。

关键词: 深度学习, 终身学习, 计算机视觉, 灾难性遗忘, 稳定性-可塑性困境

CLC Number:

TP391.4

Yichi CHEN, Bin CHEN. Review of lifelong learning in computer vision[J]. Journal of Computer Applications, 2023, 43(6): 1785-1795.

陈一驰, 陈斌. 计算机视觉中的终身学习综述[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1785-1795.

Figures/Tables 7

Fig. 1 Schematic diagram of LLL system

Tab. 1 Classification of lifelong learning methods

主要方法	子方法
基于数据驱动的终身学习	存储任务子集
基于数据驱动的终身学习	生成任务数据
基于优化过程的终身学习	损失函数设计
	梯度更新
	学习率更新
基于网络结构的终身学习	静态结构
基于网络结构的终身学习	动态结构
基于知识组合的终身学习	以上方法的组合

Fig. 2 Schematic diagram of optimization of EWC method

Fig. 3 Schematic diagram of optimization of LwF method

Fig. 4 Schematic diagram of PNN architecture

Fig. 5 Lifelong learning in object detection

Fig. 6 Lifelong learning in semantic segmentation

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Review of lifelong learning in computer vision

计算机视觉中的终身学习综述

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