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An unsupervised adversarial domain adaptation based on variational auto-encoder

MACHINE LEARNING 2025年第5期114卷 1-26页

作者： Zonoozi, Mahta Hassan Pour Seydi, Vahid Deypir, Mahmood Islamic Azad Univ Fac Tech & Engn South Tehran Branch Tehran Iran Bangor Univ Ctr Appl Marine Sci Sch Ocean Sci Bangor Wales

Collecting a large amount of labeled data in machine learning is always challenging. Often, even with sufficient data, domain differences can cause a shift or bias in data distribution, affecting model performance during testing. Domain adaptation methods, especially adversarial techniques, are effective solutions for these challenges. The goal is to learn a classifier for an unlabeled target dataset using a labeled source dataset, enhancing resistance to domain shifts. However, existing methods sometimes struggle with adapting the joint feature distribution across domains, resulting in negative transfer. To address this, we propose a method that forms class-specific clusters to prevent negative transfer. This method is encapsulated in an unsupervised adversarial domain adaptation framework based on a variational auto-encoder. Our structure is designed to enhance invariant and discriminative feature representation. We process source and target data through a VAE to establish a smooth latent representation. In our method, source and target data are fed into a variational auto-encoder, which produces a smooth latent representation. The feature extractor then plays an adversarial minimax game with the discriminator to learn domain-invariant features, while the feature extractor is shared between the reconstructed source and reconstructed target data. In addition, we proposed a second structure in which the domain discriminator part of the prior structure is eliminated to demonstrate the influence of the variational auto-encoder in domain adaptation. On numerous unsupervised domain adaptation benchmarks, our results indicate that our proposed model outperforms or is comparable to state-of-the-art outcomes.

关键词： Unsupervised domain adaptation Adversarial learning Domain shift variational auto-encoder

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Geological type recognition for shield machine using a semi-supervised variational auto-encoder-based adversarial method

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2025年 156卷

作者： Wang, Haodi Qin, Chengjin Yu, Honggan Liu, Chengliang Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Wuhan Univ Wuhan 430072 Hubei Peoples R China

In the process of tunneling, accurate and timely recognition of the geological type is significant to optimize the control parameters of the tunneling machine, improving tunneling efficiency and avoiding accidents. The shield machine operator in shield tunneling machine cannot directly observe the geological environment due to the closed working environment, so the soft method that can indirectly recognize the geological type by the machine parameters has become a research hotspot. However, most current soft methods use only a small amount of labeled data for supervised learning, and large amounts of unlabeled data is wasted. In order to use all data to improve the recognition performance of the classifier, a semi-supervised variational auto-encoder-based adversarial method (VAE-EMGAN) is proposed. Firstly, 50 parameters associated with geological types are selected and pre-processed, then the variational auto-encoder (VAE) is trained by unlabeled data, and the generated part of VAE is added to the structure of Enhanced Multi-Classification Adversarial Generative Network (EMGAN) as a generator. Finally, the recognition accuracy of classifier is improved through adversarial training with labeled data, unlabeled data and generated data. We used data from upper and lower tunnels in Singapore to create two tasks to verify the validity and generalization performance of VEVE-EMGAN. The results show that the proposed model not only achieves high accuracy of all test sets on both tasks, but also has much better generalization performance than other models. Mean accuracy is 10.82%, 17.68%, 11.05%, 17.72%, 17.45%, 12.68% and 5.27% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task A;Mean accuracy is 13.06%, 12.80%, 7.64%, 18.31%, 8.74%, 7.94% and 4.05% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task B. In particular, the performance of the adversarial trained classifier is better than that has the same

关键词： Geological type recognition Shield machine Enhanced Multi-Classification Adversarial Generative Network variational auto-encoder Adversarial training

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variational auto-encoder for text generation 5

Variational Auto-Encoder for text generation

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Hu, Haojin Liao, Mengfan Mao, Weiming Liu, Wei Zhang, Chao Jing, Yanmei Yunnan Normal Univ Sch Phys & Elect Informat Kunming Yunnan Peoples R China Lib Yunnan Normal Univ Kunming Yunnan Peoples R China

ISBN: (纸本)9781728143231;9781728143224

Many different methods to text generation have been introduced in the past. Recurrent neural network language(RNNLM) is powerful and scalable for text generation in unsupervised generative modeling. We extended the RNNLM and propose the variational auto-encoder Recurrent Neural Network(VAE-RNNLM), which designed to explicitly capture such global features as continuous latent variable. Maximum likelihood learning in such a model presents an intractable inference problem. VAE-RNNLM circumvents these difficulties by using the architecture of the latest advance in variational inference to introduce a practical training technique for powerful neural network generative models with latent variables. In this paper, we using VAE-RNNLM for text generation and achieved good performance.

关键词： variational auto-encoder recurrent neural network text generation

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Feature-aided pose estimation approach based on variational auto-encoder structure for spacecrafts

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Chinese Journal of Aeronautics 2024年第8期37卷 329-341页

作者： Yanfang LIU Rui ZHOU Desong DU Shuqing CAO Naiming QI Department of Aerospace Engineering Harbin Institute of TechnologyHarbin 150001China Suzhou Research Institute of HIT Suzhou 215104China Shanghai Institute of Spaceflight Control Technology Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China

Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit *** from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose *** improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on variational auto-encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on variational auto-encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target *** methods treat the pose vector as latent variables,employing an encoder-decoder network with a variational auto-encoder(VAE)*** enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole ***,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired *** evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.

关键词： Pose estimation variational auto-encoder Feature-aided Pose Estimation Approach On-orbit measurement tasks Simulated and experimental dataset

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A novel deep learning framework with variational auto-encoder for indoor air quality prediction

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Frontiers of Environmental Science & Engineering 2024年第1期18卷 97-109页

作者： Qiyue Wu Yun Geng Xinyuan Wang Dongsheng Wang ChangKyoo Yoo Hongbin Liu Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control College of Light Industry and Food EngineeringGuangxi UniversityNanning 530004China Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province Shandong Huatai Paper Co.Ltd.Dongying 257335China Department of Environmental Science and Engineering College of EngineeringKyung Hee UniversityYongin 446701Republic of Korea

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

关键词： Indoor air quality PM_(2.5)concentration variational auto-encoder Latent variable Soft measurement modeling

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Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第5-6期119卷 4109-4123页

作者： Liu, Bo Li, Hongkun Ou, Jiayu Wang, Zhaodong Sun, Wei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

In milling processing, the wear state of the tool has an essential influence on the processing quality. The machining process is not continuous in the cycloid milling process, and the signal of the empty tool part increases the difficulty of identifying the tool wear state. At present, most of the researchers use experimental data and cut out the signal of the empty tool part in the signal by data processing. However, it will affect the original signal to a certain extent and destroy the confidential information in the original signal. A novel method using variational auto-encoder (VAE) for tool wear status identification is proposed. Due to VAE has structural characteristics that reduce the dimensionality of high-dimensional data to lower dimensionality. This requires VAE to find and learn the significant features, which are hidden in the complex raw data. In this paper, the signals of the empty tool do not need to be cut out;the effective value of the three-phase current signals obtained in the real processing is converted into the form of three-dimensional color images. VAE is applied to extract features from the image samples and then realize the classification of different wear states of the tool. A large number of comparison experiments are conducted, and the result shows that the presented method has a better recognition performance for the actual processing data. It is more suitable for the recognition of tool wear status in the actual milling process.

关键词： Tool wear states recognition variational auto-encoder Extreme learning machine Three-phase current signals

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t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

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SOFT COMPUTING 2021年第22期25卷 14183-14207页

作者： Dey, Prasanjit Saurabh, K. Kumar, C. Pandit, D. Chaulya, S. K. Ray, S. K. Prasad, G. M. Mandal, S. K. Cent Inst Min & Fuel Res CSIR Dhanbad 826001 Bihar India

A deep learning network is introduced to predict concentrations of gases in the underground coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas chamber. The air is sucked automatically at specific intervals from the sealed-off site utilizing a solenoid valve, suction pump, and programmed microprocessor. The gas sensors monitor the gas content in the underground coal mine and communicate gas concentration to the surface server room through a wireless network and cloud storage media. The t-SNE_VAE_bi-LSTM model is proposed in this study as a prediction model that combines the t-SNE, VAE, and bi-LSTM networks. The proposed model's t-SNE method aims to minimize the dimensionality of the recorded gas concentration;and VAE layer intends to retrieve the inner characteristics of low-dimensional gas concentration. Finally, the given model's Bi-LSTM layer tries to forecast the concentrations of CH4, CO2, CO, O-2, and H-2 gases. The proposed model's prediction accuracy is compared with the existing two models, namely auto-regressive integrated average moving (ARIMA) and chaos time series (CHAOS). The experiment findings demonstrate that the t-SNE_VAE_bi-LSTM model forecasted mean square error (MSE) is more accurate, and it has lesser MSE value of 0.029 and 0.069 for CH4;0.037 and 0.019 for CO2;0.092 and 0.92 for CO;1.881 and 1.892 for O-2;and 1.235 and 1.200 for H-2 than the ARIMA and CHAOS models, respectively.

关键词： variational auto-encoder Bi-LSTM t-SNE IoT Gas concentration Sealed-off area Underground coal mine

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Disentangled Graph variational auto-encoder for Multimodal Recommendation With Interpretability

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 7543-7554页

作者： Zhou, Xin Miao, Chunyan Nanyang Technol Univ Alibaba NTU Singapore Joint Res Inst Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Multimodal recommender systems amalgamate multimodal information (e.g., textual descriptions, images) into a collaborative filtering framework to provide more accurate recommendations. While the incorporation of multimodal information could enhance the interpretability of these systems, current multimodal models represent users and items utilizing entangled numerical vectors, rendering them arduous to interpret. To address this, we propose a Disentangled Graph variational auto-encoder (DGVAE) that aims to enhance both model and recommendation interpretability. DGVAE initially projects multimodal information into textual contents, such as converting images to text, by harnessing state-of-the-art multimodal pre-training technologies. It then constructs a frozen item-item graph and encodes the contents and interactions into two sets of disentangled representations utilizing a simplified residual graph convolutional network. DGVAE further regularizes these disentangled representations through mutual information maximization, aligning the representations derived from the interactions between users and items with those learned from textual content. This alignment facilitates the interpretation of user binary interactions via text. Our empirical analysis conducted on three real-world datasets demonstrates that DGVAE significantly surpasses the performance of state-of-the-art baselines by a margin of 10.02%. We also furnish a case study from a real-world dataset to illustrate the interpretability of DGVAE.

关键词： Numerical models Vectors Visualization Image reconstruction Transformers Semantics Mutual information Multimodal recommendation variational auto-encoder disentangled learning interpretability

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A trajectory outlier detection method based on variational auto-encoder

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 15075-15093页

作者： Zhang, Longmei Lu, Wei Xue, Feng Chang, Yanshuo Xian Univ Sci & Technol Sch Commun & Informat Engn Xian 710054 Peoples R China Xian Univ Finance & Econ Sch Informat Xian 710100 Peoples R China

Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In this work, we proposed a trajectory outlier detection model based on variational auto-encoder. First, the model encodes the trajectory data as parameters of distribution functions based on the statistical characteristics of urban traffic. Then, an auto-encoder network is built and trained. The training goal of the auto-encoder network is to maximize the generation probability of original trajectories when decoding. Once the model training is completed, we can detect the trajectory outlier by the difference between a trajectory and the trajectory generated by the model. The advantage of the proposed model is that it only needs to compute the difference between the original trajectory and the trajectory generated by the model when detecting the trajectory outlier, which greatly reduces the amount of calculation and makes the model very suitable for real-time detection scenarios. In addition, the distance threshold between the abnormal trajectory and the normal trajectory can be set by referring to the proportion of the abnormal trajectory in the training data set, which eliminates the difficulty of setting the threshold manually and makes the model more convenient to be applied in different actual scenes. In terms of effect, the proposed model has achieved more than 95% in accuracy, which is better than the two typical density based and classification-based detection methods, and also better than the methods based on machine learning in recent years. In terms of efficiency, the model has good convergence in the training phase and the training time increases slowly with the data scale, which is better than or as the same as the comparison methods.

关键词： trajectory outlier detection machine learning variational auto-encoder trajectory similarity

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Information concentrated variational auto-encoder for quality-related nonlinear process monitoring

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JOURNAL OF PROCESS CONTROL 2020年 94卷 12-25页

作者： Zhu, Jiazhen Shi, Hongbo Song, Bing Tao, Yang Tan, Shuai East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes a novel algorithm named information concentrated variational auto-encoder (IFCVAE). To concentrate the quality-related information, a loading matrix regularization based on mutual information is designed, so that the strongly quality-related variables tend to have larger weights in the loading matrix. In addition, to monitor processes from the quality-related and unrelated aspects, IFCVAE decomposes the original space into two subspaces that are mutually orthogonal based on variational auto-encoder (VAE). With the help of an additional regression network, the two subspaces can correspond to the quality-related and unrelated spaces. For process monitoring, two statistics are designed for the subspaces according to Kullback-Leibler divergence. Finally, the effectiveness of IFCVAE is demonstrated by a numerical case and an industrial case. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Process monitoring variational auto-encoder Quality-related Feature extraction

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