迁移自适应学习综述


论文题目:Transfer Adaptation Learning: A Decade Survey

论文作者:Lei Zhang

会议期刊:arxiv 2019


介绍

在很多实际的情况中, 源域(source domain)目标域(target domain)之间存在:

  • 分布不匹配(distribution mismatch)
  • 领域偏移(domain shift)

独立同分布(independent identical distribution, i.i.d)的假设不再满足!

  • 迁移学习(transfer learning)假设源域与目标域拥有不同的联合概率分布

  • 领域自适应(domain adaptation)假设源域与目标域拥有不同的边缘概率分布, 但拥有相同的条件概率分布


实例权重调整自适应

当训练集和测试集来自不同分布时, 这通常被称为采样选择偏差(sample selection bias)或者协方差偏移(covariant shift).

实例权重调整方法旨在通过非参数方式对跨域特征分布匹配直接推断出重采样的权重.

基于直觉的权重调整

直接对原始数据进行权重调整.

首次提出于NLP领域1, 主要的方法有著名的TrAdaBoost2.

基于核映射的权重调整

将原始数据映射到高维空间(如,再生核希尔伯特空间RKHS)中进行权重调整.

分布匹配

主要思想是通过重新采样源数据的权重来匹配再生核希尔伯特空间中源数据和目标数据之间的均值.

主要有两种非参数统计量来衡量分布差异:

  • 核均值匹配(kernel mean matching, KMM)

Huang等人3首次提出通过调整源样本的$\beta$权重系数, 使得带权源样本和目标样本的KMM最小.

  • 最大均值差异(maximum mean discrepancy, MMD)45

weighted MMD6方法考虑了类别权重偏差.

样本选择

主要方法有基于k-means聚类的KMapWeighted7, 基于MMD和$\ell_{2,1}$-norm的TJM8等.

协同训练

主要思想是假设数据集被表征为两个不同的视角, 使两个分类器独立地从每个视角中进行学习.

主要方法有CODA9, 以及基于GAN的RANN10等.


特征自适应

特征自适应方法旨在寻找多源数据(multiple sources)的共同特征表示.

基于特征子空间

该方法假设数据可以被低维线性子空间进行表示, 即低维的格拉斯曼流形(Grassmann manifold)被嵌入到高维数据中.

通常用PCA方法来构造该流形, 使得源域和目标域可以看成流形上的两个点, 并得到两者的测地线距离(geodesic flow).

  • 基于流形的方法有SGF11GFK12

  • 基于子空间对齐的方法有SA13,SDA14GTH15

基于特征变换

特征变换方法旨在学习变换或投影矩阵,使得源域和目标域中的数据在某种分布度量准则下更接近.

基于投影

该方法通过减少不同域之间的边缘分布和条件分布差异, 求解出最优的投影矩阵.

主要方法有:

  • 基于边缘分布MMD的TCA16, 条件分布MMD的JDA17

  • 基于布拉格曼散度(Bregman divergence)的TSL18

  • 基于希尔伯特-施密特独立性(Hilbert-Schmidt Independence Criterion)19

基于度量

该方法通过在带标签的源域中学习一个好的距离度量, 使得其能够应用于相关但不同的目标域中.

主要方法有:

  • 基于一阶统计量的RTML20

  • 基于二阶统计量的CORAL21

基于增强

该方法假设数据的特征被分为三种类型:公共特征/源域特征/目标域特征.

主要方法有:

  • 基于零填充(Zero Padding)的EasyAdapt(EA)22
  • 基于生成式模型(Generative Model)23

基于特征重构

主要方法有:

  • 低秩重构(Low-rank Reconstruction)24
  • 稀疏重构(Sparse Reconstruction)25

基于特征编码

主要方法有:

  • 共享域字典(Domain-shared dictionary)26
  • 指定域字典(Domain-specific dictionary)27

分类器自适应

分类器自适应旨在利用源域中带标签数据和目标域中少量带标签数据学习一个通用的分类器.

基于核分类器

主要方法有:

  • 自适应支持向量机(adaptive support vector machine, ASVM)28

  • 基于多核学习(multiple kernel learning, MKL)的域迁移分类器29

基于流形正则项

主要方法有ARTL30,DMM31,MEDA32等.

基于贝叶斯分类器

主要方法有核贝叶斯迁移学习KBTL33等.


深度网络自适应

2014年, Yosinski等人34讨论了深度神经网络中不同层特征的可迁移特性.

基于边缘分布对齐

主要方法有:

  • 深度域混淆DDC35
  • 深度自适应网络DAN36
  • 联合自适应网络JAN37, 同时提出了Joint MMD准则

基于条件分布对齐

主要方法有深度迁移网络DTN38

基于自动编码器

主要方法有边缘堆叠式降噪自动编码器mSDA39


对抗式自适应

通过对抗目标(如,域判别器)来减少域间差异.

基于梯度转换

Ganin等人40首次提出可以通过添加一个简单的梯度转换层(gradient reversal layer, GRL)来实现领域自适应.

基于Minimax优化

Ajakan等人41首次结合分类损失和对抗目标, 提出了DANN方法.

其它方法还有:

  • 对抗判别领域自适应ADDA42

  • 条件领域对抗网络CDAN43

  • 最大分类器差异MCD44

基于生成对抗网络

主要方法有:

  • CyCADA45

  • Duplex GAN46

基准数据集

  • Office-31 (3DA)
  • Office+Caltech-10 (4DA)
  • MNIST+USPS
  • Multi-PIE
  • COIL-20
  • MSRC+VOC2007
  • IVLSC
  • Cross-dataset Testbed
  • Office HomeNEW
  • ImageCLEF
  • P-A-C-SNEW

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