自编码器及其应用综述

doi:10.11959/j.issn.1000-436x.2021160

Abstract

Abstract:

As a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many other fields.Thus, the theoretical basis, improved methods, application fields and research directions of autoencoder were described and summarized comprehensively.At first, the network structure, theoretical derivation and algorithm flow of traditional autoencoder were introduced and analyzed, and the difference between autoencoder and other unsupervised learning algorithms was compared.Then, common improved autoencoders were discussed, and their innovation, improvement methods and relative merits were analyzed.Next, the practical application status of autoencoder in target recognition, intrusion detection and other fields were introduced.At last, the existing problems of autoencoder were summarized, and the possible research directions were prospected.

Key words: autoencoder, deep learning, unsupervised learning, feature extraction, regularization

CLC Number:

TP183

Jie LAI, Xiaodan WANG, Qian XIANG, Yafei SONG, Wen QUAN. Review on autoencoder and its application[J]. Journal on Communications, 2021, 42(9): 218-230.

Figures/Tables 6

算法	出发点	改进方式
传统正则化自编码器	调整权值衰减程度，改善过拟合现象	在自编码器损失函数中添加权值的L₂范数正则化项
稀疏自编码器	增强所提取特征的稀疏性	在自编码器损失函数中添加隐含层输出的KL散度正则化项
去噪自编码器	改善噪声影响，提升所提取特征的稳健性	引入退化过程，用添加噪声的输入样本重构原始纯净样本
收缩自编码器	提升所提取特征对输入小扰动的稳健性	在自编码器损失函数中添加隐含层输出的收缩正则化项
变分自编码器	迫使隐变量满足特定分布，具备数据生成能力	添加隐变量分布的KL正则化项，引入重参数技巧将采样计算变为数值计算
卷积自编码器	保留图像的空间信息	将自编码器的全连接层替换为卷积层与池化层
极限学习机-自编码器	改善泛化性能，提升训练效率	随机赋值隐含层输入权值与偏置，并计算输出权值的最小二乘解
区分自编码器	增强所提取特征的可区分性	在自编码器损失函数中添加隐含层输出的类内距离与类间距离正则化项
L₂₁范数自编码器	兼顾所提取特征的稀疏性与稳健性	在自编码器损失函数中添加隐含层输出的L₂₁范数正则化项
对抗自编码器	迫使隐变量满足特定分布，提升数据生成能力	引入对抗学习策略，完成隐变量向特定分布的逼近
图自编码器	保持低维流形的局部一致性	通过引入迹函数，在自编码器损失函数中添加图正则化项
二次自编码器	提升所提取抽象特征的稳健性	采用输入样本的二次运算代替传统神经节点的内积，提出了二次神经节点，并构建其自编码器结构
狄利克雷变分自编码器	提升生成样本的对数似然性，避免解码器权值崩溃	将变分自编码器假设的高斯分布替换为狄利克雷分布，利用随机梯度下降拟合其分布函数
注意力协同自编码器	提升局部关键信息的重要程度	将注意力机制引入自编码器中，在反向传播中实现关键特征的重新加权
同构自编码器	降低重构误差，使抽象特征更能描述输入样本数据结构	在堆栈自编码器中，将各层自编码器期望输出均替换为原始输入样本

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算法	目的	实现方式	特点
AE	特征提取特征降维	由编码器与解码器组成，前者完成样本的抽象特征表示，后者完成输入样本的重构，通过梯度下降算法完成迭代微调	确定型无监督学习模型，能有效提取高级抽象特征，但所提取特征的稀疏性、稳健性不足
RBM	特征提取特征降维	由可见层与隐含层组成，旨在最大化可见变量与隐含变量联合概率分布的似然函数，通过对比散度算法完成迭代微调	概率型无监督学习模型，能有效提取高级抽象特征，但易产生过拟合现象
GAN	数据生成	由生成器与判别器组成，旨在通过生成器与判别器的二元博弈提升生成样本质量，网络参数的优化由梯度下降算法完成	无监督生成模型，能生成高质量样本，但易发生模式崩溃问题
SOM	聚类分析特征提取特征降维	由输入层与竞争层组成，旨在通过竞争学习策略逐步优化网络，近邻关系函数维持输入样本的拓扑结构，进而完成样本的低维映射	浅层无监督学习模型，具有独特的竞争学习机制，能有效生成样本的低维映射，但不适用于高维特征的提取

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