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摘要: 用于复杂工业过程难测运行指标和异常故障建模的样本具有量少稀缺、分布不平衡以及内涵机理知识匮乏等特性. 虚拟样本生成(Virtual sample generation, VSG)作为扩充建模样本数量及其涵盖空间的技术, 已成为解决上述问题的主要手段之一, 但已有研究还存在缺乏理论支撑、分类准则与应用边界模糊等问题. 本文在描述复杂工业过程难测运行指标和异常故障建模所存在问题的基础上, 梳理虚拟样本定义及其内涵, 给出面向工业过程回归与分类问题的VSG实现流程; 接着, 从样本覆盖区域、实现流程与推广应用等方向进行综述; 然后, 分析讨论VSG的下一步研究方向; 最后, 对全文进行总结并给出未来挑战.Abstract: The modeling samples for difficulty to measure operation indexes and abnormal faults of complex industrial processes usually have the characteristics of sparse quantity, unbalanced distribution, and lack of connotation mechanism knowledge. Virtual sample generation (VSG) is a technology to expand the space and quantity of modeling samples and has become one of the main ways to solve the formerly mentioned difficulties. However, there are still some problems in the existing research results, such as the lack of theoretical support, the unclear of category criterion and the application boundary. First, the existing problems for difficulty to measure operational indexes and abnormal fault modeling of complex industrial processes are described. The definition of virtual samples and the connotation of virtual samples are combed, and the VSG implementation process for the regression and classification problems is provided. Second, the research status is summarized from the sample coverage area, implementation process, and application. Third, further research direction is analyzed and discussed. Finally, the summary and future challenges are given out.
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图 1 Web of Science上的VSG论文数量与被引频次
Fig. 1 Number and citation frequency of articles on VSG in Web of Science
图 2 样本输入空间内虚拟与真实样本间的关系
Fig. 2 Relationship between virtual samples and real samples in sample input space
图 3 三维空间下的不同虚拟样本输入生成方法示意图
Fig. 3 Diagram of different virtual sample input generation methods in 3D space
图 4 映射模型生成虚拟样本输出流程图
Fig. 4 Flow chart of virtual sample output generation based on mapping model
图 5 面向分类问题的虚拟与真实样本间的关系
Fig. 5 Relationship between virtual samples and real samples for classification problem
图 10 面向VSG的原始域、可扩展域和未知域的示意图
Fig. 10 Schematic diagram of original, extension, and unknown domain for VSG
图 13 面向回归建模问题的VSG应用统计结果
Fig. 13 VSG application statistical results for regression modeling problem
图 14 2019 ~ 2022年面向故障诊断领域的VSG应用统计结果
Fig. 14 VSG application statistical results for fault diagnosis on 2019 ~ 2022
表 2 面向回归问题VSG的合成数据集
Table 2 Synthetic datasets of VSG for regression problem
基准函数 取值空间 文献 $y = \left\{ {\begin{aligned} &{\sin x/x,{\rm{ if }}x \ne 0}\\ &{1,{\rm{ if }}x = 0} \end{aligned}} \right.$ $x \in \left[ { - 2\pi ,2\pi } \right]$ [49] $\begin{aligned} y = \;&2.077\;5 + 9.045\;46 \times \left( {{{10}^{ - 1}}} \right){x_1} + x_2^2 + \cos \left( {{x_3}} \right) + 1.355\;6 \times \left( {1.5 \times \left( {1 - {x_4}} \right)} \right){\rm{ }} +\\ &x_5^3 + {x_6} - 2.571\;51{x_7} - 5.097\;36 \times \left( {{{10}^{ - 1}}} \right) \times \left( {x_8^2} \right)\end{aligned}$ $x \in \left[ {0,1} \right]$ [53] $\begin{aligned}y = \;&0.415\sin {x_1} - 0.312x_2^2 + 1/\left( {1 + {{\rm{e}}^{ - {x_3}}}} \right) + \cos x_4^3 + 0.66{{\rm{e}}^{1 - x_5^{0.5}}}\sin {x_5}{\rm{ }} \;-\\ &\cos {x_6}\ln \left( {1/\cos {x_6}} \right) + 0.38\tanh {x_7} + \left( {1 - x_8^3} \right)\cos x_8^3\end{aligned}$ ${\rm{ }}x \in \left[ {0,1} \right]$ [54] $\begin{aligned} &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right)\\ &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,0.01{x^2} } \right)\\ &y = \;x + 0.2\sin \left( {20x} \right) + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right) \end{aligned}$ $x \in \left[ {0,1} \right]$ [55] $\begin{aligned} y =\;& 1.335\;6 \times \left( {1.5\left( {1 - {x_1} } \right)} \right) + { {\rm{e} }^{2{x_1} - 1} }\sin \left( {3\pi { {\left( { {x_1} - 0.6} \right)}^2} } \right){\rm{ } } +\\ &{ {\rm{e} }^{3\left( { {x_2} - 0.5} \right)} }\sin \left( {4\pi { {\left( { {x_2} - 0.9} \right)}^2} } \right)\end{aligned}$ $x \in \left[ {0,1} \right]$ [57, 64, 127] $y = \sin \left( {{x_1}} \right) + \cos \left( {{x_2}} \right) + \sin \left( {{x_1}} \right) \times \cos \left( {{x_2}} \right)$ ${x_1} \in \left[ { - \pi ,\pi } \right]$ [59, 129] ${x_2} \in \left[ {0,2\pi } \right]$ $y = {{\rm{e}}^{(2x - 1)}}\sin \left[ {4\pi {{(x - 0.6)}^2}} \right] + \varepsilon ,{\rm{ }}\varepsilon \ \sim {\rm{N}}(0,0.002\;5)$ $x \in \left[ {0,1} \right]$ [20] 注: $\varepsilon $是为了更好地模拟实际工业过程的环境影响而添加的噪声项. 
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表 3 面向分类问题的VSG公开数据集
Table 3 Public datasets of VSG for classification problem
数据集 数据集信息 文献 Case Western Reserve University 由美国凯斯西储大学发布的位于轴承数据中心网站的轴承故障 [67, 69, 92, 132−133] (CWRU)轴承故障数据集1 数据集, 包含无故障和滚动体、内圈和外圈故障数据 University of Connecticut 美国康涅狄格大学Jiong Tang团队发布的齿轮箱故障数据集, [85] (UoC)齿轮箱故障 包括健康工况、缺齿、齿根裂纹、齿面剥落以及 数据集2 不同程度齿尖破损状态数据 Tennessee Eastman process 由美国伊士曼化学公司开发的化学过程模拟平台生成的 [156−157] (TEP)数据集3 数据集, 包括正常工况和21种异常工况数据 IEEE PHM 2009齿轮箱故障 由2009年的IEEE PHM挑战赛提供的齿轮箱故障 [80] 数据集4 数据集, 包含健康、缺齿、齿裂等8种工况 西安交通大学Spectra Quest (SQ) 由西安交通大学SQ实验平台得到的电机轴承外圈 [150] 轴承故障数据集5 和内圈故障数据集 数据集网址: 1 https://engineering.case.edu/bearingdatacenter/download-data-file 2 https://figshare.com/articles/dataset/Gear_Fault_Data/6127874/1 3 http://depts.washington.edu/control/LARRY/TE/download.html 4 http://www.phmsociety.org/references/datasets 5 https://github.com/sliu7102/SQ-dataset-with-variable-speed-for-fault-diagnosis
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A1 VSG的研究成果统计与对比
A1 Statistics and comparison of VSG research results
分类 子分类 方法 年份 优劣 文献 面向样本覆盖区域之原始域样本空间
回归VSG特征工程 LLE + BPNN 2020 特征变换, 流形学习更加直观, 但特征失去物理含义 [52] Isomap + 插值法 2020 [53] t-SNE + RF 2021 [54] 机理 2021 特征选择, 工业过程知识获取困难 [55] 两者结合 2020 综合特征变换与选择, 具有较强的定制化特性 [56] 样本工程 空间投影 + RBF 2021 函数模型, 空间投影具有新颖性 [57] 数据趋势 2021 函数模型, 提出的稀疏假设和集中假设具有参考价值 [49] 总线拓扑结构插值 2023 函数模型, 有效控制插值位置 [58] RWNN插值法 2018 函数模型, 基于神经网络 [59] AANN插值 2019 模型学习样本的非线性分布关系 [60] RWNN + 等间隔插值法 2020 对小样本难以有效 [56] MTD + PSO 2021 函数模型, PSO优化选择虚拟样本 [61] 多目标PSO 2022 函数模型, 多目标PSO优化选择虚拟样本和生成数量 [15] LOF + K-means + GAN 2021 对抗模型, 插值生成输出, CGAN生成输入 [20] 双GAN 2022 对抗模型, 两种GAN分别负责输入和
输出的生成, 复杂性高[64] 回归器 + CWGAN 2022 对抗模型, 通过回归器匹配虚拟样本输出并共同训练 [65] 面向样本覆盖区域之原始域样本空间的分类VSG 特征工程 添加编码器 2020 采用编码器提取特征 [68] 添加卷积层 2021 添加卷积层提取特征 [66] 添加卷积层 2021 [67] 添加自注意 2022 添加自注意力模型增强特征 [69] 面向样本覆盖区域之原始域样本空间的分类VSG 样本工程 基于加权核的SMOTE 2018 函数模型, 解决SMOTE算法在高IR下
的非线性可分离问题[71] Minkowski距离替换欧氏距离 2019 函数模型, 有效生成高维虚拟样本 [72] SMOTE + 决策树 2020 函数模型, 决策树算法提取关键规则 [73] SMOTE + SVM 2020 函数模型, 支持向量边界生成虚拟样本 [74] 范围控制SMOTE 2021 函数模型, 有效地缓解范围偏移和边界样本重叠等问题 [75] 超球面空间 + 组发现技术 2007 函数模型, 由数据的结构生成虚拟样本 [76] 组发现技术 + 纯化过程 2020 函数模型, 纯化过程剔除冗余样本 [77] ACGAN 2020 对抗模型, 添加Dropout层防止过拟合, 添加卷积层
提取更多特征[79] ACWGAN-GP 2020 对抗模型, ACGAN的进化版 [80] MAML + ACGAN 2021 对抗模型, MAML初始化和更新网络使得生成过程
更加稳定[81] GAN + 多尺度CNN 2021 对抗模型, 生成模型需要改进 [82] DCGAN + K-means 2021 对抗模型, K-means算法对模型改进 [67] MoGAN 2021 对抗模型, 判别器既判断样本真假又
充当分类器和故障检测器[83] GAN + MSCNN 2021 对抗模型, 多GAN联合生成 [82] 贝叶斯优化 + WGAN 2021 对抗模型, 贝叶斯优化策略自适应调节判别器参数 [86] WGAN + LSTM-FCN 2022 对抗模型, 结合LSTM [84] AE + GAN 2019 对抗模型, AE结合GAN [89] VAE + GAN 2020 [68] 深度残差网络 + VAE + GAN 2021 对抗模型, 深度残差网络提高模型性能 [90] AE + LSGAN 2022 对抗模型, 暹罗编码器计算特征残差 [91] CVAEGAN-SM 2022 对抗模型, 生成器中加入自调制机制 [92] 堆叠AE + WGAN 2023 对抗模型, 提升了模型的生成能力 [93] 面向样本覆盖区域之扩展域样本空间的
回归VSG集合理论 正态隶属度 1997 模糊集理论, 仅适用于扩展范围对称情况 [94] DNN 2003 模糊集理论, 特征相关系数大于0.9才能计算扩展范围 [95] MTD 2007 模糊集理论, 通过假设特征独立不对称地扩散特征范围 [96] GTD 2010 模糊集理论, 增量版的MTD [97] TTD 2012 模糊集理论, 与树算法结合 [98] 神经网络MTD 2012 模糊集理论, 神经网络与MTD结合 [99] MD-MTD 2016 模糊集理论, 三角和均匀分布组合的多分布 [100] KNN + MTD 2022 模糊集理论, KNN确保合理的扩展范围 [101] K-means + MTD 2022 模糊集理论, K-means解决属性冗余 [102] AD-MTD + MD-MTD 2019 模糊集理论, 多种算法结合取长补短 [103] MTD + RWNN 2020 模糊集理论, 改进分布 + 隐含层插值 [56] MTD + GA 2014 模糊集理论, 基于优化算法搜寻虚拟样本, 更合理 [46] TMIE + PSO 2016 模糊集理论, PSO优化选择虚拟样本 [104] MTD + PSO 2021 [61] 分布假设 IKDE 2006 高斯分布, 改进KDE分布 [109] 时序IKDE 2008 高斯分布, 用于时序数据 [110] SJDT 2016 高斯分布, SJDT将数据趋于正态分布 [111] MPV 2013 非高斯分布, 多样本分布 [112] 假设检验 2019 非高斯分布, 先聚类再估计 [113] 基于知识 多目标PSO 2022 基于知识确定输出扩展域下限, 多目标PSO优化选择虚拟样本和生成数量 [15] 面向样本覆盖区域之扩展域样本空间的
分类VSG集合理论 FID 2017 模糊集理论, 既生成虚拟样本又填充缺失 [114] SMOTE + 粗糙集理论 2012 粗糙集理论, 扩展范围有限 [115] 三支决策 2018 粗糙集理论, 未精准计算扩展范围 [116] 分布假设 假设分布 2010 高斯分布, 计算数据的均值和方差确定高斯分布 [43] 假设分布 2022 高斯分布, AIC和BIC自适应确定高斯分布参数 [117] SVM 2013 非高斯分布, 状态函数采样生成虚拟样本 [118] K-means + Weibull分布 2014 非高斯分布, 特定过程采用特定分布 [119] 基于知识 FAGAN 2021 基于知识, 专家知识定义的故障属性作为辅助信息以
使得生成样本[123] 面向VSG实现流程之回归问题 过程数据预处理阶段 缺失值删减和人工填充 2021 有效减少缺失和异常值对数据的影响但会减少样本数量 [61] 2022 [15] 缺失和异常值识别剔除 2020 [127] 2022 [64] LLE 2020 流形学习更加直观, 特征失去物理含义 [52] Isomap 2020 [53] t-SNE 2021 [54] 根据化工机理选择特征 2017 机理知识获取困难 [128] 2018 [59] 专家经验 2021 特定实验知识 [61] 虚拟样本输入生成阶段 欧氏距离识别稀疏区域 2020 引入欧氏距离 [127] 投影最大间距识别稀疏 2021 引入投影最大间距 [57] 可视化样本分布识别稀疏区域 2020 可视化, 直观 [52] 2020 [53] 2021 [54] 稀疏性和集中性假设 2021 确定稀疏和密集区域关系 [49] WGAN-GP 2022 引入GAN用于回归 [64] CWGAN 2022 [65] MTD 2007 确定虚拟样本输入的扩展域范围后插值 [96] TMIE + PSO 2016 [104] 流形子空间 + MTD 2021 [129] 虚拟样本输出生成阶段 RWNN映射模型 2018 映射模型的性能受限于小样本 [59] 2016 [104] 2021 [129] BPNN映射模型 2020 [52] RF映射模型 2021 [54] RBF映射模型 2021 [57] CS-CGAN匹配输出 2022 匹配模型与虚拟样本输入同时训练 [64] 回归器匹配输出 2022 [65] 分位数回归器匹配输出 2021 [55] 虚拟样本质量筛选阶段 模型误差小于10%筛选 2014 受限于小样本建模性能 [46] 隶属度的似然估计筛选 2018 引入似然估计 [130] PSO优化算法筛选 2021 引入优化算法 [61] 专家筛选 2021 具有主观性 [49] 虚拟样本数量确定阶段 信息熵 2019 引入信息熵 [131] 稀疏和集中假设 2021 引入各种假设 [49] 特殊阶段 LOF + CGAN 2021 先生成虚拟样本输出后匹配虚拟样本输入 [20] 三样条插值 + ITNN 2021 [49] 面向VSG实现流程之分类问题 过程数据预处理阶段 信号数据转换为灰度图 2022 借鉴图像领域算法处理 [85] 2022 [132] 虚拟样本输入生成阶段 重叠分割、旋转和抖动的
数据增强2021 缓解过拟合 [133] SMOTE + 粗糙集理论 2012 扩展范围有限 [115] SMOTE + 决策树 2020 决策树算法提取运行规则 [73] SMOTE + SVM 2020 引入支持向量机边界 [74] Minkowski距离替换欧氏距离 2019 可以有效地生成高维虚拟样本 [72] 范围控制SMOTE 2021 通过控制生成范围减少边界重叠样本 [75] WGAN + LSTM-FCN 2022 引入LSTM [84] CWGAN-GP + FDGRU 2022 增加梯度惩罚项和条件信息 [85] Pull-away损失函数GAN 2022 添加自注意力模型增强特征 [69] ACGAN 2020 添加Dropout层防止过拟合, 添加卷积层提取更多特征 [79] ACGAN + CVAE 2020 引入CVAE [68] 深度残差网络 + VAE + GAN 2021 深度残差网络提高模型性能 [90] MoGAN 2021 判别器包含真假判断、故障诊断和故障分类三种功能 [83] CVAEGAN-SM 2022 生成器加入自调制机制 [92] 并行GAN 2020 对应多类别同时训练, 复杂性高 [19] SMOTE + VAE 2018 基于样本的迁移学习VSG [134] 自适应混合 2020 [135] 迁移学习 + 插值 2022 [136] Fine-tuning + WGAN 2021 基于模型的迁移学习VSG [137] 迁移学习 + GAN 2022 [138] 虚拟样本质量筛选阶段 Wasserstein距离 2020 未给出评价指标的具体限值筛选虚拟样本 [139] KL散度, F-score, 2021 [66] Kappa系数, GAN测试值 Wasserstein距离, KL
散度, 欧氏距离,2021 [67] 皮尔逊相关系数 马氏距离, 欧氏距离 2021 给出评价指标的具体限值筛选虚拟样本 [82] 判别概率, 最大均值差异, KL散度 2022 [69] 皮尔逊相关系数 2022 未给出评价指标的具体限值筛选虚拟样本 [85] 最大均值差异, KL散度, 2022 [92] GAN测试值 虚拟样本数量确定阶段 分类复杂度确定虚拟样本数量 1998 采用分类复杂度确定虚拟样本数量 [14] 面向VSG推广应用的回归问题 石油化工 TMIE + PSO 2016 PSO优化选择 [104] RWNN插值法 2018 提出隐含层插值生成虚拟样本 [59] Isomap + 插值法 2020 [53] 分位数回归器匹配输出 2021 提出分位数回归匹配输出 [55] 回归器 + CWGAN 2022 通过回归器匹配虚拟样本输出并同时训练 [65] 固废焚烧 两者结合 2020 具有较强的定制化特性 [56] MTD + PSO 2021 PSO优化选择虚拟样本 [61] 多目标PSO 2022 多目标PSO优化选择虚拟样本和生成数量 [15] 工业制造 GTD 2010 增量版的MTD [97] TTD 2012 与树算法结合 [98] MPV 2013 采用多分布 [112] 模糊c均值聚类 + 箱线图 2018 箱线图确定扩展范围 [141] 假设分布 2022 AIC和BIC自适应确定高斯分布参数 [117] 矿业冶金 时频变换 + FBP +
信息熵2018 特定问题采用特定方法 [6] RWNN插值 + MD-MTD 2019 GA优化选择虚拟混合样本 [145] 面向VSG推广应用的分类问题 滚动轴承
故障诊断迁移学习 + GAN 2020 迁移与GAN相结合 [146] PGDAE + DCN 2021 引入PGDAE [147] 元学习 + WAE 2021 元学习提高虚拟样本质量 [66] CVAEGAN-SM 2022 生成器加入自调制机制 [92] DSAN 2022 自注意模块增强深度特征 [132] GAN 2022 常数Q转换将信号转换为频谱图, 均方差替换交叉熵 [148] ACGAN 2022 引入ACGAN [149] 特征增强GAN 2022 自注意模块增强深度特征 [69] DFGN 2021 可用于零样本故障诊断 [150] 变压器
故障诊断SMOTE + 决策树 2020 决策树算法提取关键规则 [73] SMOTE + SVM 2020 提出支持向量边界生成样本 [74] CWGAN-GP 2020 引入梯度惩罚 [151] AE + LSGAN 2022 暹罗编码器计算特征残差 [91] 涡轮机
故障诊断GAN 2019 结合GAN与具体问题 [152] DACNN 2019 [153] VAE + GAN 2019 [89] 1D-CNN GAN 2019 虚拟样本输出和故障诊断组合模型 [154] 齿轮箱
故障诊断ACGAN + CVAE 2020 引入CVAE [68] 贝叶斯优化 + WGAN 2021 贝叶斯优化策略自适应调节判别器参数 [86] DCGAN + K-means 2021 K-means算法对模型改进 [67]
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A2 符号说明
A2 Symbol description
缩写词 英文全称 中文全称 VSG Virtual sample generation 虚拟样本生成 MSWI Municipal solid waste incineration 城市固废焚烧 DXN Dioxin 二噁英 VAE Variational autoencoder 变分自编码器 GAN Generative adversarial network 生成对抗网络 FDD Fault detection and diagnosis 故障检测与诊断 IR Imbalance ratio 不平衡比 SMOTE Synthetic minority over-sampling technique 合成少数类过采样技术 MAPE Mean absolute percentage error 平均绝对百分比误差 LLE Locally linear embedding 局部线性嵌入 BPNN Back propagation neural network 反向传播神经网络 Isomap Isometric feature mapping 等距特征映射 t-SNE t-distributed stochastic neighbor embedding t分布随机邻域嵌入 RF Random forest 随机森林 RBF Radial basis function 径向基函数 CSI Cubic spline interpolation 三样条插值 ITNN Input-training neural network 输入训练神经网络 RWNN Random weight neural network 随机权神经网络 AANN Auto-associative neural network 自联想神经网络 LOF Local outlier factor 局部异常因子 CGAN Conditional generative adversarial network 条件生成对抗网络 CS-CGAN Cycle structure conditional generative adversarial network 循环结构条件生成对抗网络 FFT Fast Fourier transform 快速傅里叶变换 SVM Support vector machine 支持向量机 AC-GAN Auxiliary classifier generative adversarial network 辅助分类器生成对抗网络 ACWGAN-GP Auxiliary classier Wasserstein generative adversarial network with 具有梯度惩罚的辅助分类Wasserstein生成对抗网络 gradient penalty MAML Model agnostic meta learning 模型无关元学习 CNN Convolutional neural network 卷积神经网络 DCGAN Deep convolutional generative adversarial network 深度卷积生成对抗网络 MoGAN Minority oversampling generative adversarial network 少数类过采样生成对抗网络 AE Autoencoder 自编码器 CVAE-GAN Conditional variational autoencoder generative adversarial network 条件变分自编码器生成对抗网络 LSGAN Least squares generative adversarial network 最小二乘生成对抗网络 DNN Diffusion neural network 扩散神经网络 MTD Mega-trend-diffusion 大趋势扩散 GTD Generalized-trend-diffusion 广义趋势扩散 TTD Tree structure based trend diffusion 树结构趋势扩散 MD-MTD Multi-distribution mega-trend-diffusion 多分布大趋势扩散 KNN K-nearest neighbor K近邻 AD-MTD Advanced mega-trend-diffusion 改进型大趋势扩散 Hybrid-MTD Hybrid mega-trend-diffusion 混合大趋势扩散 GA Genetic algorithm 遗传算法 FBP Feasibility-based programming 可行性的规划 TMIE Information-expanded based on triangular membership 基于三角隶属度的信息扩散 PSO Particle swarm optimization 粒子群优化 IKDE Improved kernel density estimation 改善核密度估计 SJDT Small Johnson data transformation 小型约翰变换方法 MPV Maximal p value 最大p值 AIC Akaike information criterion 赤池信息准则 AICc Corrected version of the akaike information criterion 修正版赤池信息准则 FID Fuzzy-based information decomposition 基于模糊的信息分解 BIC Bayesian information criterion 贝叶斯信息准则 FAGAN Fault attributes generative adversarial network 故障属性生成对抗网络 SRWGAN Semantic refinement Wasserstein generative adversarial network 语义细化Wasserstein生成对抗网络 MOPSO Multi-objective particle swarm optimization 多目标粒子群优化 PGDAE Predictive generative denoising autoencoder 预测生成去噪自编码器 DCN Deep coral network 深度珊瑚网络 WAE Wasserstein autoencoder Wasserstein自编码器 DSAN Deep subdomain adaptation network 深度子域适应网络 DFGN Deep feature generating network 深度特征生成网络 DACNN Deep adversarial convolutional neural network 深度对抗卷积神经网络 BO Bayesian optimization 贝叶斯优化 DCGAN Deep convolution generative adversarial network 深度卷积生成对抗网络
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