深度域适应综述: 一般情况与复杂情况

范苍宁; 刘鹏; 肖婷; 赵巍; 唐降龙

doi:10.16383/j.aas.c200238

深度域适应综述: 一般情况与复杂情况

doi: 10.16383/j.aas.c200238

范苍宁^1,,
刘鹏^1,,
肖婷^1,,
赵巍^1,,
唐降龙^1,

1.
哈尔滨工业大学计算机科学与技术学院模式识别与智能系统研究中心哈尔滨 150001

基金项目: 国家自然科学基金(61671175), 四川省科技计划项目基金(2019YFS0069), 空间智能控制技术重点实验室基金(ZDSXS-2018-02)资助

详细信息

作者简介:
范苍宁：哈尔滨工业大学模式识别与智能系统研究中心博士研究生. 分别于2016年和2018年获得哈尔滨工业大学学士学位和硕士学位. 主要研究方向为迁移学习和机器学习. 本文通信作者. E-mail: fancangning@gmail.com

刘鹏：哈尔滨工业大学计算机科学与技术学院副教授. 2007年获得哈尔滨工业大学微电子和固体电子学博士学位. 主要研究方向为图像处理, 视频分析, 模式识别和大规模集成电路设计. E-mail: pengliu@hit.edu.cn

肖婷：哈尔滨工业大学计算机科学与技术学院博士研究生. 2016年获得哈尔滨工业大学计算机应用硕士学位. 主要研究方向为图像处理, 计算机视觉和机器学习. E-mail: xiaoting1@hit.edu.cn

赵巍：哈尔滨工业大学计算机科学与技术学院副教授. 曾获黑龙江省科学技术进步一等奖. 主要研究方向为模式识别, 机器学习和计算机视觉. E-mail: zhaowei@hit.edu.cn

唐降龙：哈尔滨工业大学计算机科学与技术学院教授. 1995年获得哈尔滨工业大学计算机应用技术博士学位. 主要研究方向为模式识别, 图像处理和机器学习. E-mail: tangxl@hit.edu.cn

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出版历程
- 收稿日期: 2020-04-22
- 录用日期: 2020-09-14
- 网络出版日期: 2021-04-02
- 刊出日期: 2021-04-02

A Review of Deep Domain Adaptation: General Situation and Complex Situation

1.
Pattern Recognition and Intelligent System Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001

Funds: Supported by National Natural Science Foundation of China (61671175), Science and Technology Planning Project of Sichuan Province (2019YFS0069), and Space Intelligent Control Technology Key Laboratory Foundation Project (ZDSXS-2018-02)

More Information

Author Bio:
FAN Cang-Ning　Ph.D. candidate at the Pattern Recognition and Intelligent System Research Center, Harbin Institute of Technology. He received his bachelor and master degrees from Harbin Institute of Technology, in 2016 and in 2018, respectively. His research interest covers transfer learning and machine learning. Corresponding author of this paper

LIU Peng　Associate professor at the School of Computer Science and Technology, Harbin Institute of Technology. He received his Ph.D. degree in microelectronics and solid-state electronics from Harbin Institute of Technology in 2007. His research interest covers image processing, video analysis, pattern recognition, and design of large scale integrated circuits

XIAO Ting　Ph.D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology. She received her master degree in computer application technology from Harbin Institute of Technology in 2016. Her research interest covers image processing, computer vision, and machine learning

ZHAO Wei　Associate professor at the School of Computer Science and Technology, Harbin Institute of Technology. She won a First Prize of Heilongjiang Province Science and Technology Progress. Her research interest covers pattern recognition, machine learning, and computer vision

TANG Xiang-Long　Professor at the School of Computer Science and Technology, Harbin Institute of Technology. He received his Ph.D. degree in computer application technology from Harbin Institute of Technology in 1995. His research interset covers pattern recognition, image processing, and machine learning

摘要

摘要:
信息时代产生的大量数据使机器学习技术成功地应用于许多领域. 大多数机器学习技术需要满足训练集与测试集独立同分布的假设, 但在实际应用中这个假设很难满足. 域适应是一种在训练集和测试集不满足独立同分布条件下的机器学习技术. 一般情况下的域适应只适用于源域目标域特征空间与标签空间都相同的情况, 然而实际上这个条件很难满足. 为了增强域适应技术的适用性, 复杂情况下的域适应逐渐成为研究热点, 其中标签空间不一致和复杂目标域情况下的域适应技术是近年来的新兴方向. 随着深度学习技术的崛起, 深度域适应已经成为域适应研究领域中的主流方法. 本文对一般情况与复杂情况下的深度域适应的研究进展进行综述, 对其缺点进行总结, 并对其未来的发展趋势进行预测. 首先对迁移学习相关概念进行介绍, 然后分别对一般情况与复杂情况下的域适应、域适应技术的应用以及域适应方法性能的实验结果进行综述, 最后对域适应领域的未来发展趋势进行展望并对全文内容进行总结.
- 域适应 /
- 迁移学习 /
- 深度域适应 /
- 深度学习 /
- 机器学习
Abstract:
The large amount of data generated in the information age enables machine learning to be successfully applied in many fields. Most machine learning techniques need to meet the assumption that the training set and the test set are independent and identically distributed, but in practice this assumption is difficult to meet. Domain adaptation is a machine learning technology in which the training set and test set do not need to satisfy the condition of independent and identical distribution. The general domain adaptation is only applicable to the case where feature space and label space of the source domain and target domain are the same, but in fact this condition is difficult to meet. In order to enhance the applicability of domain adaptation, domain adaptation under complex conditions has gradually become a research hotspot. Domain adaptation under the condition of inconsistent label space and complex target domain is an emerging direction in recent years. With the rise of deep learning technology, deep domain adaptation has become the mainstream method in the field of domain adaptation research. This article reviews the research progress of deep domain adaptation in general and complex situations, summarizes their shortcomings, and predicts their future development trends. This article firstly introduces the concepts of transfer learning, and then summarizes domain adaptation in general and complex situations, the application of domain adaptation technology and the performance of domain adaptation methods, finally prospects the development trend of the domain adaptation field and summarizes the content of the full text.
- Domain adaptation /
- transfer learning /
- deep domain adaptation /
- deep learning /
- machine learning

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图 1 本文的组织结构

Fig. 1 The structure of this article

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图 2 部分使用MMD的深度域适应方法

Fig. 2 Some deep domain adaptation methods based on MMD

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图 3 领域对抗神经网络的网络结构

Fig. 3 The network structure of DANN

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图 4 条件领域对抗网络

Fig. 4 Conditional domain adversarial network

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图 5 领域分离网络

Fig. 5 Domain separation network

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图 6 文献[92]所使用的网络结构

Fig. 6 The network structure used in [92]

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图 7 CycleGAN的训练过程((a)源域图像通过翻译网络G变换到目标域, 目标域图像通过翻译网络F变换到源域;(b)在源域中计算循环一致性损失; (c)在目标域中计算循环一致性损失)

Fig. 7 The training process of CycleGAN ((a) source images are transformed to target domain through translation network G, target images are transformed to source domain through translation network F; (b) calculate the cycle-consistency loss in source domain; (c) calculate the cycle-consistency loss in target domain.)

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图 8 选择对抗网络的网络结构

Fig. 8 The network structure of SAN

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图 9 文献[111]中的网络结构

Fig. 9 The network structure in [111]

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图 10 通用适配网络的训练过程

Fig. 10 The training process of univerial adaptation network

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图 11 文献[113]中的网络结构

Fig. 11 The network structure in [113]

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图 12 文献[112]使用元学习来提取泛化性能优异的特征

Fig. 12 Reference [112] uses meta learning to extract features with excellent generalization performance

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表 1 深度域适应的四类方法

Table 1 Four kinds of methods for deep domain adaptation

四类深度域适应方法	特点	典型方法
基于领域分布差异的方法	通过最小化两个领域分布差异, 提取领域不变性特征	深度适配网络 (DAN)^[20]
基于对抗的方法	通过对抗学习来对齐分布	多对抗域适应 (MADA)^[2]
基于重构的方法	抑制信息损失/解耦特征	领域分离网络 (DSN)^[5]
基于样本生成的方法	合成符合目标域分布的假样本协助训练	循环生成对抗网络 (CycleGAN)^[21]

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表 2 标签空间不一致的域适应问题

Table 2 Domain adaptation with inconsistent label space

不同标签空间集合关系下的域适应	问题特点	典型方法
部分域适应	目标域标签空间是源域标签空间的子集	选择对抗网络(SAN)^[1]
开集域适应	源域标签空间是目标域标签空间的子集	分离适配(STA)^[105]
通用域适应	源域与目标域标签空间集合关系无法确定	通用适配网络(UDA)^[106]

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表 3 复杂目标域情况下的域适应问题

Table 3 Domain adaptation in the case of complex target domain

复杂目标域情况下的域适应	问题特点	典型方法
多目标域域适应	目标域样本来自于多个子目标域	多目标域适配网络 (MTDA-ITA)^[3]
领域泛化	目标域样本不可得; 目标域未知	特征评价网络 (Feature-critic network)^[112]

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表 4 在Office31数据集上各深度域适应方法的准确率 (%)

Table 4 Accuracy of each deep domain adaptation method on Office31 dataset (%)

方法	${\rm{A}}\to {\rm{W}}$	${\rm{D}}\to {\rm{W}}$	${\rm{W}}\to {\rm{D}}$	${\rm{A}}\to {\rm{D}}$	${\rm{D}}\to {\rm{A}}$	${\rm{W}}\to {\rm{A}}$	平均
ResNet50^[26]	68.4	96.7	99.3	68.9	62.5	60.7	76.1
DAN^[20]	80.5	97.1	99.6	78.6	63.6	62.8	80.4
DCORAL^[42]	79.0	98.0	100.0	82.7	65.3	64.5	81.6
RTN^[39]	84.5	96.8	99.4	77.5	66.2	64.8	81.6
DANN^[27]	82.0	96.9	99.1	79.7	68.2	67.4	82.2
ADDA^[75]	86.2	96.2	98.4	77.8	69.5	68.9	82.9
JAN^[23]	85.4	97.4	99.8	84.7	68.6	70.0	84.3
MADA^[2]	90.1	97.4	99.6	87.8	70.3	66.4	85.2
GTA^[99]	89.5	97.9	99.8	87.7	72.8	71.4	86.5
CDAN^[22]	94.1	98.6	100.0	92.9	71.0	69.3	87.7

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表 5 在OfficeHome数据集上各深度域适应方法的准确率 (%)

Table 5 Accuracy of each deep domain adaptation method on OfficeHome dataset (%)

方法	${\rm{A}}\to {\rm{C}}$	${\rm{A}}\to {\rm{P}}$	${\rm{A}}\to {\rm{R}}$	${\rm{C}}\to {\rm{A}}$	${\rm{C}}\to {\rm{P}}$	${\rm{C}}\to {\rm{R}}$
ResNet50^[26]	34.9	50.0	58.0	37.4	41.9	46.2
DAN^[20]	43.6	57.0	67.9	45.8	56.5	60.4
DANN^[27]	45.6	59.3	70.1	47.0	58.5	60.9
JAN^[23]	45.9	61.2	68.9	50.4	59.7	61.0
CDAN^[22]	50.7	70.6	76.0	57.6	70.0	70.0
方法	${\rm{P}}\to {\rm{A}}$	${\rm{P}}\to {\rm{C }}$	${\rm{P}}\to {\rm{R}}$	${\rm{R}}\to {\rm{A}}$	${\rm{R}}\to {\rm{C }}$	${\rm{R}}\to {\rm{P}}$	平均
ResNet50^[26]	38.5	31.2	60.4	53.9	41.2	59.9	46.1
DAN^[20]	44.0	43.6	67.7	63.1	51.5	74.3	56.3
DANN^[27]	46.1	43.7	68.5	63.2	51.8	76.8	57.6
JAN^[23]	45.8	43.4	70.3	63.9	52.4	76.8	58.3
CDAN^[22]	57.4	50.9	77.3	70.9	56.7	81.6	65.8

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表 6 在Office31数据集上各部分域适应方法的准确率 (%)

Table 6 Accuracy of each partial domain adaptation method on Office31 dataset (%)

方法	${\rm{A}}\to {\rm{W}}$	${\rm{D}}\to {\rm{W}}$	${\rm{W}}\to {\rm{D}}$	${\rm{A}}\to {\rm{D}}$	${\rm{D}}\to {\rm{A}}$	${\rm{W}}\to {\rm{A}}$	平均
ResNet50^[26]	75.5	96.2	98.0	83.4	83.9	84.9	87.0
DAN^[20]	59.3	73.9	90.4	61.7	74.9	67.6	71.3
DANN^[27]	73.5	96.2	98.7	81.5	82.7	86.1	86.5
IWAN^[109]	89.1	99.3	99.3	90.4	95.6	94.2	94.6
SAN^[1]	93.9	99.3	99.3	94.2	94.1	88.7	94.9
PADA^[107]	86.5	99.3	100.0	82.1	92.6	95.4	92.6
ETN^[108]	94.5	100.0	100.0	95.0	96.2	94.6	96.7

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表 7 在Office31数据集上各开集域适应方法的准确率 (%)

Table 7 Accuracy of each open set domain adaptation method on Office31 dataset (%)

方法	${\rm{A}}\to {\rm{W}}$		${\rm{A}}\to {\rm{D}}$		${\rm{D}}\to {\rm{W}}$
方法	OS	OS*	OS	OS*	OS	OS*
ResNet50^[26]	82.5	82.7	85.2	85.5	94.1	94.3
RTN^[39]	85.6	88.1	89.5	90.1	94.8	96.2
DANN^[27]	85.3	87.7	86.5	87.7	97.5	98.3
OpenMax^[145]	87.4	87.5	87.1	88.4	96.1	96.2
ATI-$ \lambda $^[110]	87.4	88.9	84.3	86.6	93.6	95.3
OSBP^[111]	86.5	87.6	88.6	89.2	97.0	96.5
STA^[105]	89.5	92.1	93.7	96.1	97.5	96.5
方法	${\rm{W}}\to {\rm{D}}$		${\rm{D}}\to {\rm{A}}$		${\rm{W}}\to {\rm{A}}$		平均
方法	OS	OS*	OS	OS*	OS	OS*	OS	OS*
ResNet50^[26]	96.6	97.0	71.6	71.5	75.5	75.2	84.2	84.4
RTN^[39]	97.1	98.7	72.3	72.8	73.5	73.9	85.4	86.8
DANN^[27]	99.5	100.0	75.7	76.2	74.9	75.6	86.6	87.6
OpenMax^[145]	98.4	98.5	83.4	82.1	82.8	82.8	89.0	89.3
ATI-$ \lambda $^[110]	96.5	98.7	78.0	79.6	80.4	81.4	86.7	88.4
OSBP^[111]	97.9	98.7	88.9	90.6	85.8	84.9	90.8	91.3
STA^[105]	99.5	99.6	89.1	93.5	87.9	87.4	92.9	94.1

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表 8 在OfficeHome数据集上通用域适应及其他方法的准确率 (%)

Table 8 Accuracy of universal domain adaptation and other methods on OfficeHome dataset (%)

方法	${\rm{A}}\to {\rm{C}}$	${\rm{A}}\to {\rm{P}}$	${\rm{A}}\to {\rm{R}}$	${\rm{C}}\to {\rm{A}}$	${\rm{C}}\to {\rm{P}}$	${\rm{C}}\to {\rm{R}}$
ResNet^[26]	59.4	76.6	87.5	69.9	71.1	81.7
DANN^[27]	56.2	81.7	86.9	68.7	73.4	83.8
RTN^[39]	50.5	77.8	86.9	65.1	73.4	85.1
IWAN^[109]	52.6	81.4	86.5	70.6	71.0	85.3
PADA^[107]	39.6	69.4	76.3	62.6	67.4	77.5
ATI-$ \lambda $^[110]	52.9	80.4	85.9	71.1	72.4	84.4
OSBP^[111]	47.8	60.9	76.8	59.2	61.6	74.3
UAN^[106]	63.0	82.8	87.9	76.9	78.7	85.4
方法	${\rm{P}}\to {\rm{A}}$	${\rm{P}}\to {\rm{C}}$	${\rm{P}}\to {\rm{R}}$	${\rm{R}}\to {\rm{A}}$	${\rm{R}}\to {\rm{C}}$	${\rm{R}}\to {\rm{P}}$	平均
ResNet^[26]	73.7	56.3	86.1	78.7	59.2	78.6	73.2
DANN^[27]	69.9	56.8	85.8	79.4	57.3	78.3	73.2
RTN^[39]	67.9	45.2	85.5	79.2	55.6	78.8	70.9
IWAN^[109]	74.9	57.3	85.1	77.5	59.7	78.9	73.4
PADA^[107]	48.4	35.8	79.6	75.9	44.5	78.1	62.9
ATI-$ \lambda $^[110]	74.3	57.8	85.6	76.1	60.2	78.4	73.3
OSBP^[111]	61.7	44.5	79.3	70.6	55.0	75.2	63.9
UAN^[106]	78.2	58.6	86.8	83.4	63.2	79.4	77.0

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表 9 在Office31数据集上AMEAN及其他方法的准确率 (%)

Table 9 Accuracy of AMEAN and other methods on Office31 dataset (%)

方法	${\rm{A}}\to {\rm{D,W}}$	${\rm{D}}\to {\rm{A,W}}$	${\rm{W}}\to {\rm{A,D}}$	平均
ResNet^[26]	68.6	70.0	66.5	68.4
DAN^[20]	78.0	64.4	66.7	69.7
RTN^[39]	84.3	67.5	64.8	72.2
JAN^[23]	84.2	74.4	72.0	76.9
AMEAN^[113]	90.1	77.0	73.4	80.2

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表 10 在Office31数据集上DADA及其他方法的准确率 (%)

Table 10 Accuracy of DADA and other methods on Office31 dataset (%)

方法	${\rm{A} }\to {\rm{C} }, $$ \;{\rm{ D},\;\rm{W} }$	${\rm{C} }\to {\rm{A} }, $$ \; {\rm{D},\;\rm{W} }$	${\rm{D} }\to {\rm{A} },\; $$ {\rm{C},\;{\rm{W} } }$	${\rm{W} }\to {\rm{A} }, $$ \;{\rm{C},\;\rm{D} }$	平均
ResNet^[26]	90.5	94.3	88.7	82.5	89.0
MCD^[28]	91.7	95.3	89.5	84.3	90.2
DANN^[27]	91.5	94.3	90.5	86.3	90.6
DADA^[4]	92.0	95.1	91.3	93.1	92.9

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表 11 在MNIST数据集上领域泛化方法的准确率 (%)

Table 11 Accuracy of domain generalization methods on MNIST dataset (%)

源域	目标域	DAE	DICA	D-MTAE	MMD-AAE
${M}_{ {15} },\;{M}_{30},\;{M}_{45},\;{M}_{60},\;{M}_{75}$	$ {M}_{0} $	76.9	70.3	82.5	83.7
${M}_{ {0} },\;{M}_{30},\;{M}_{45},\;{M}_{60},\;{M}_{ {75} }$	$ {M}_{15} $	93.2	88.9	96.3	96.9
${M}_{ {0}},\;{M}_{15},\;{M}_{45},\;{M}_{60},\;{M}_{{75} }$	$ {M}_{30} $	91.3	90.4	93.4	95.7
${M}_{ {0}},\;{M}_{15},\;{M}_{30},\;{M}_{60},\;{M}_{{75} }$	$ {M}_{45} $	81.1	80.1	78.6	85.2
${M}_{ {0}},\;{M}_{15},\;{M}_{30},\;{M}_{45},\;{M}_{{75} }$	$ {M}_{60} $	92.8	88.5	94.2	95.9
${M}_{ {0}},\;{M}_{15},\;{M}_{30},\;{M}_{45},\;{M}_{{60} }$	$ {M}_{75} $	76.5	71.3	80.5	81.2
平均		85.3	81.6	87.6	89.8

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