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adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

KNOWLEDGE-BASED SYSTEMS 2020年 190卷 105187-105187页

作者： Wang, Xuhong Du, Ying Lin, Shijie Cui, Ping Shen, Yuntian Yang, Yupu Shanghai Jiao Tong Univ Shanghai Peoples R China Wuhan Univ Wuhan Peoples R China Univ Calif Davis Davis CA 95616 USA

Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Moreover, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a self-adversarial variational autoencoder (adVAE) with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of a variational autoencoder, a generator G tries to distinguish between the normal latent variables encoded by E and the anomalous latent variables synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate, but also become an additional regularization mechanism to prevent overfitting. Compared with other competitive methods, the proposed model achieves significant improvements in extensive experiments. The employed datasets and our model are available in a Github repository. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Outlier detection Novelty detection Deep generative model variational autoencoder

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SeqVAE: Sequence variational autoencoder with policy gradient

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APPLIED INTELLIGENCE 2021年第12期51卷 9030-9037页

作者： Gao, Ting Cui, Yidong Ding, Fanyu Beijing Univ Posts & Telecommun Coll Comp Beijing Peoples R China

In the paper, we propose a variant of variational autoencoder (VAE) for sequence generation task, called SeqVAE, which is a combination of recurrent VAE and policy gradient in reinforcement learning. The goal of SeqVAE is to reduce the deviation of the optimization goal of VAE, which we achieved by adding the policy-gradient loss to SeqVAE. In the paper, we give two ways to calculate the policy-gradient loss, one is from SeqGAN and the other is proposed by us. In the experiments on them, our proposed method is better than all baselines, and experiments show that SeqVAE can alleviate the "post-collapse" problem. Essentially, SeqVAE can be regarded as a combination of VAE and Generative Adversarial Net (GAN) and has better learning ability than the plain VAE because of the increased adversarial process. Finally, an application of our SeqVAE to music melody generation is available online(1)(2).

关键词： Sequence generation task variational autoencoder Generative Adversarial net SeqVAE

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Anomalous Change Detection in Drilling Process Using variational autoencoder with Temperature Near Drill Edge

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INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY 2023年第5期17卷 449-457页

作者： Suwa, Haruhiko Oda, Kazuya Murakami, Koji Setsunan Univ Dept Mech Engn 17-8 Ikedanaka Machi Neyagawa Osaka 5728508 Japan Setsunan Univ Grad Sch Sci & Engn Div Ind Dev Engn Neyagawa Japan Yamamoto Met Technos Co Ltd Osaka Japan

The different flexibility and diversity requirements for respective manufacturing units have made modern cutting tool management much more crucial and complicated, as a greater variety of tools and more frequent tool changes are required to enhance production efficiency and avoid unplanned manufacturing downtime. Developing in-process anomalous change detection methods has been identified as an essential challenge. Machine learning techniques have been widely applied in tool condition monitoring and anomalous change detection. As anomaly data is rare in manufacturing processes, supervised machine learning approaches (such as regression and classification) are not applied to the anomalous change detection problem. Rather, self-supervised machine learning (a representative type of unsupervised machine learning) is applied. This study describes a variational autoencoder (VAE) neural network and proposes a VAE-based method for tool condition monitoring and change detection in a drilling process using the temperature near a drill edge. The proposed VAE evaluates the drill tool condition based on the reconstruction error between the input temperature and its estimate per a drill unit process through the trained network. Computational simulations demonstrate that the proposed VAE network model can avoid overfitting to the anomaly data and that its expressive power is greater than that of the conventional autoencoder model.

关键词： anomalous change detection drilling pro-cess machine learning variational autoencoder tempera-ture near drill edge

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Rejecting Unknown Gestures Based on Surface-Electromyography Using variational autoencoder

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 750-758页

作者： Dai, Qingfeng Wong, Yongkang Kankanhalli, Mohan Li, Xiangdong Geng, Weidong Zhejiang Univ Coll Comp Sci & Technol State Key Lab CAD & CG Hangzhou 310058 Peoples R China Natl Univ Singapore Sch Comp Singapore 117417 Singapore Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China

The conventional surface electromyography (sEMG)-based gesture recognition systems exhibit impressive performance in controlled laboratory settings. As most systems are trained in a closed-set setting, the systems's performance may see significant deterioration when novel gestures are presented as imposter. In addition, the state-of-the-art generative and discriminative methods have achieved considerable performance on high-density sEMG signals. This can be seen as an unrealistic setting as the real-world muscle computer interface are mainly comprised of sparse multichannel sEMG signals. In this work, we propose a novel variational autoencoder based approach for open-set gesture recognition based on sparse multichannel sEMG signals. Using the predefined corresponding latent conditional distribution of known gestures, the conditional Gaussian distribution of each known gesture is learned. Those samples with low probability density are identified as unknown gestures. The sEMG signals of known gestures are classified using the Kullback-Leibler divergences between the predefined prior distributions and input samples. The proposed approach is evaluated using three benchmark sparse multichannel sEMG databases. The experimental results demonstrate that our approach outperforms the existing open-set sEMG-based gesture recognition approaches and achieves a better trade-off between classifying known gestures and rejecting unknown gestures.

关键词： Open-set recognition gesture recognition surface electromyography variational autoencoder

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Distributed temporal-spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant-wide process monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第5期102卷 1917-1931页

作者： Yao, Zongyu Jiang, Qingchao Gu, Xingsheng Pan, Chunjian East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Shanghai Univ Elect Power Coll Automat Engn Shanghai Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China

Process monitoring plays an essential role in ensuring the safe and efficient operation of multi-unit plantwide industrial processes. However, complicated nonlinear dynamics of these processes pose enormous challenges to the plantwide process monitoring approaches. In this paper, a novel distributed temporal-spatial neighbourhood enhanced variational autoencoder (DTS-VAE) monitoring scheme is proposed. The scheme selects the temporal and spatial neighbourhood sets of a current sample from a local unit. Time-series correlation is utilized to construct temporal representative samples to obtain process dynamic characteristics. Likewise, spatial similarity is employed to construct spatial representative samples to obtain spatial patterns in the data. Then the reconstructed and current samples serve as the inputs for a variational autoencoder to extract features from the current sample and its neighbourhoods. Subsequently, a distributed monitor that considers the temporal-spatial characteristics is established for each unit. Finally, a comprehensive evaluation index is developed using Bayesian fusion strategy to improve monitoring performance. The proposed DTS-VAE monitoring scheme was effectively verified on the Tennessee Eastman benchmark process and a wastewater treatment plant.

关键词： dynamic process nonlinear process process monitoring temporal-spatial neighbourhood variational autoencoder

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A Physically Constrained variational autoencoder for Geochemical Pattern Recognition

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MATHEMATICAL GEOSCIENCES 2022年第4期54卷 783-806页

作者： Xiong, Yihui Zuo, Renguang Luo, Zijing Wang, Xueqiu China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China Minist Nat Resources Key Lab Geochem Explorat Inst Geophys & Geochem Explorat Langfang 065000 Peoples R China UNESCO Int Ctr Global Scale Geochem Langfang 065000 Peoples R China

Quantification and recognition of geochemical patterns are extremely important for geochemical prospecting and can facilitate a better understanding of regional metallogenesis. Recognition of such patterns with deep learning (DL) algorithms has attracted considerable attention, as these algorithms can generally extract high-level geochemical features and thus create models in which geochemical patterns are fully exploited. These DL algorithms are usually constructed to be compatible and consistent with the underlying data, but their interpretability and pertinent physical constraints (such as granitic intrusions related to magmatic-hydrothermal mineralization) are generally overlooked. This paper introduces a physically constrained variational autoencoder (VAE) architecture to identify geochemical patterns associated with tungsten polymetallic mineralization. We first identify physical constraints from geological characteristics and metallogenic regulation via the methods of fry analysis, standard deviation ellipses, and fractal analysis to reveal the controlling function of granitic intrusions on mineralization. Subsequently, we construct the physical constraints based on the nonlinear controlling function and add the geological constraints into the VAE loss function as a penalty term. After optimization of the network parameters, the well-trained physically constrained VAE architecture can recognize the geochemical anomaly patterns that the conventional VAE cannot. These extracted geochemical anomaly patterns generally show a strong spatial relationship with the granitic intrusions. In addition, the performance measures involving the receiver operating characteristic curve and success-rate further indicate that the generalization accuracy of the conventional VAE can be enhanced via physics-based regularization. These results suggest that the proposed physically constrained VAE, which integrates physical knowledge into the VAE loss function, not only improves the g

关键词： Geochemical patterns Physically constrained deep learning variational autoencoder Geochemical exploration

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Nested variational autoencoder for topic modelling on microtexts with word vectors

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EXPERT SYSTEMS 2021年第2期38卷 e12639-e12639页

作者： Trinh, Trung Quan, Tho Mai, Trung Ho Chi Minh City Univ Technol Fac Comp Sci & Engn Ho Chi Minh Vietnam

Most of the information on the Internet is represented in the form ofmicrotexts, which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modelling is one of the popular methods to extract knowledge from a collection of documents;however, conventional topic models such as latent Dirichlet allocation (LDA) are unable to perform well on short documents, mostly due to the scarcity of word co-occurrence statistics embedded in the data. The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets. To solve the lack of information of microtexts, we allow our method to take advantage of word embeddings for additional knowledge of relationships between words. For speed and scalability, we apply autoencoding variational Bayes, an algorithm that can perform efficient black-box inference in probabilistic models. The result of our work is a novel topic model called thenested variational autoencoder, which is a distribution that takes into account word vectors and is parameterized by a neural network architecture. For optimization, the model is trained to approximate the posterior distribution of the original LDA model. Experiments show the improvements of our model on microtexts as well as its runtime advantage.

关键词： microtext neural network topic modelling variational autoencoder word embedding

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Identity-Aware variational autoencoder for Face Swapping

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 5466-5479页

作者： Li, Zonglin Zhang, Zhaoxin He, Shengfeng Meng, Quanling Zhang, Shengping Zhong, Bineng Ji, Rongrong Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Peoples R China Guangxi Normal Univ Sch Software Guilin 541004 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

Face swapping aims to transfer the identity of a source face to a target face image while preserving the target attributes (e.g., facial expression, head pose, illumination, and background). Most existing methods use a face recognition model to extract global features from the source face and directly fuse them with the target to generate a swapping result. However, identity-irrelevant attributes (e.g., hairstyle and facial appearances) contribute a lot to the recognition task, and thus swapping this task-specific feature inevitably interfuses source attributes with target ones. In this paper, we propose an identity-aware variational autoencoder (ID-VAE) based face swapping framework, dubbed VAFSwap, which learns disentangled identity and attribute representations for high-fidelity face swapping. In particular, we overcome the unpaired training barrier of VAE and impose a proxy identity on the latent space by exploiting the weak supervision from an auxiliary image set whose identity is averaged from multiple collected face images. To explicitly guide the identity fusion, we further devise an identity-associated matrix that corresponds different face regions with their identity representations to perform identity-related feature interactions. Finally, we incorporate spatial dimensions into the latent space and exploit the generative priors of a pre-trained face generator, allowing the effective elimination of noticeable swapping artifacts. Extensive experiments on the FaceForensics++ and CelebA-HQ datasets demonstrate that our method outperforms the state-of-the-art significantly.

关键词： Faces Face recognition Training Three-dimensional displays Decoding Task analysis Shape Face swapping variational autoencoder weak-supervised training

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Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers' purchasing behavior

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NEURAL COMPUTING & APPLICATIONS 2022年第21期34卷 18525-18541页

作者： Hasumoto, Kyosuke Goto, Masayuki Waseda Univ Inst Data Sci Tokyo Japan Waseda Univ Fac Sci & Engn Tokyo Japan

Customer churn is considered a critical issue for all businesses, as customer loss leads to a decrease in future profits. Although acquiring new customers can address such losses, this process tends to cost more than retaining existing customers. Therefore, identifying potential churners and then retaining them is important. While churn prediction has been studied widely, current research must analyze more complex business models in response to their increase, such as platform businesses. However, modeling churn prediction for these businesses is challenging because consumer behavior over platforms is more complicated. Past approaches to churn prediction can be improved upon using recent advancements in deep learning that capture the nonlinear relationships behind the data in a data-driven manner, especially for complex business models. This study proposes a method of extracting latent features from purchase histories as explanatory variables for churn prediction using a variational autoencoder with the actual customer distribution as a prior. The proposed method is validated using real purchase data from a platform business and shows a 1.5% improvement in F-measure against the baseline, and a 20% improvement for customers with recent transactions. Subsequently, the variables are examined using several methods for data analysis to interpret the meanings of the extracted features and underlying customers' purchasing behavior in the group of potential churners. With these analyses, the model provides practical implications for understanding what kind of purchasing behavior may lead to churn and planning effective retention strategies.

关键词： variational autoencoder Deep learning Churn prediction RFM Customer lifetime value Consumer purchase behavior

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Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第11-12期111卷 3597-3611页

作者： Proteau, Antoine Zemouri, Ryad Tahan, Antoine Thomas, Marc Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada HESAM Univ CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM F-750141 Paris France

In this paper, we applied a variational autoencoder approach to an industrial machining problematic. We proposed a model based on a two-steps training process and a two-dimensional latent space. This two-dimensional latent space has better dimension reduction capability compared to a principal component analysis, which would require 24 components to express 90.0% of the variation. Moreover, the proposed model is shown capable of classifying a cutting operation based solely on data obtained from sensors mounted on a CNC machine, with an accuracy of 99.24%. The suggested model is also shown capable to be an efficient visual process monitoring tool capable of detecting early changes of state in a machining process. We show that this approach can visually identify defect caused by an increase of less than 1% of the energy in the signal, which is earlier than conventional monitoring methods. Additionally, our work is based on an industrial dataset acquired during regular production. This increases the opportunity for technological transfer when it comes to better understanding and better monitoring early changes in a machining process.

关键词： variational autoencoder Dimension reduction 2D-visualization Process monitoring Early detection Machining process

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