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检索条件"主题词=Variational autoencoder"

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Vector-Quantized autoencoder With Copula for Collaborative Filtering 21

Vector-Quantized Autoencoder With Copula for Collaborative F...

引用

30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Guanyu Zhong, Ting Xu, Xovee Zhang, Kunpeng Zhou, Fan Wang, Yong Univ Elect Sci & Technol China Chengdu Peoples R China Univ Maryland College Pk MD USA Zhengzhou Aiwen Comp Technol Co Ltd Zhengzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450384469

In theory, the variational auto-encoder (VAE) is not suitable for recommendation tasks, although it has been successfully utilized for collaborative filtering (CF) models. In this paper, we propose a Gaussian Copula-Vector Quantized autoencoder (GC-VQAE) model that differs prior arts in two key ways: (1) Gaussian Copula helps to model the dependencies among latent variables which are used to construct a more complex distribution compared with the meanfield theory;and (2) by incorporating a vector quantisation method into encoders our model can learn discrete representations which are consistent with the observed data rather than directly sampling from the simple Gaussian distributions. Our approach is able to circumvent the "posterior collapse" issue and break the prior constraint to improve the flexibility of latent vector encoding and learning ability. Empirically, GC-VQAE can significantly improve the recommendation performance compared to existing state-of-the-art methods.

关键词： Recommender system vector quantisation Gaussian Copula collaborative filtering variational autoencoder

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A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems 22

A Semi-Supervised VAE Based Active Anomaly Detection Framewo...

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31st ACM Web Conference (WWW)

作者： Huang, Tao Chen, Pengfei Li, Ruipeng Tencent Shenzhen Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781450390965

Nowadays, the large online systems are constructed on the basis of microservice architecture. A failure in this architecture may cause a series of failures due to the fault propagation. Thus, the large online systems need to be monitored comprehensively to ensure the service quality. Even though many anomaly detection techniques have been proposed, few of them can be directly applied to a given microservice or cloud server in industrial environment. To settle these challenges, this paper presents SLA-VAE, a semi-supervised learning based active anomaly detection framework using variational auto-encoder. SLA-VAE first defines anomalies based on feature extraction module, introduces semi-supervised VAE to identify anomalies in multivariate time series, and employs active learning to update the online model via a small number of uncertain samples. We conduct experiments on the cloud server data from two different types of game business in Tencent (R). The results show that SLA-VAE significantly outperforms other state-of-the-art methods and is suitable for wide deployment in large online business system.

关键词： anomaly detection online systems deep learning variational autoencoder active learning

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LEVERAGING 3D INFORMATION IN UNSUPERVISED BRAIN MRI SEGMENTATION 18

LEVERAGING 3D INFORMATION IN UNSUPERVISED BRAIN MRI SEGMENTA...

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18th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Lambert, Benjamin Louis, Maxime Doyle, Senan Forbes, Florence Dojat, Michel Tucholka, Alan Pixyl Res & Dev Lab F-38000 Grenoble France Univ Grenoble Alpes INRIA CNRS Grenoble INPLJK F-38000 Grenoble France Univ Grenoble Alpes INSERM U1216 Grenoble Inst Neurosci F-38000 Grenoble France

ISBN: (纸本)9781665412469

Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuous task. To tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a variational autoencoder (VAE). Previous work on UAD adopted 2D approaches, meaning that MRIs are processed as a collection of independent slices. Yet, it does not fully exploit the spatial information contained in MRI. Here, we propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side contribution, we present a new loss function guarantying a robust training. Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions. Experiments demonstrate the interest of 3D methods which outperform their 2D counterparts.

关键词： Deep Learning variational autoencoder Anomaly Detection Medical Imaging

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Representative Data Selection for Efficient Medical Incremental Learning

Representative Data Selection for Efficient Medical Incremen...

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45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Wei, Bo-Quan Chen, Jen-Jee Tseng, Yu-Chee Kuo, Po-Tsun Paul Natl Yang Ming Chiao Tung Univ NYCU Coll Artificial Intelligence Hsinchu Taiwan Acad Sinica Taipei Taiwan Kaohsiung Med Univ Kaohsiung Taiwan Advantech Co AI Res Ctr Taipei Taiwan NYCU Coll Artificial Intelligence Hsinchu Taiwan

ISBN: (纸本)9798350324471

To train a deep neural network relies on a large amount of annotated data. In special scenarios like industry defect detection and medical imaging, it is hard to collect sufficient labeled data all at once. Newly annotated data may arrive incrementally. In practice, we also prefer our target model to improve its capability gradually as new data comes in by quick re-training. This work tackles this problem from a data selection prospective by constraining ourselves to always retrain the target model with a fix amount of data after new data comes in. A variational autoencoder (VAE) and an adversarial network are combined for data selection, achieving fast model retraining. This enables the target model to continually learn from a small training set while not losing the information learned from previous iterations, thus incrementally adapting itself to new-coming data. We validate our framework on the LGG Segmentation dataset for the semantic segmentation task.

关键词： Adversarial network continual learning data management incremental learning variational autoencoder

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Cross Domain Image Transformation Using Effective Latent Space Association 15th

Cross Domain Image Transformation Using Effective Latent Spa...

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15th International Conference on Intelligent Autonomous Systems (IAS)

作者： Ul Islam, Naeem Lee, Sukhan Sungkyunkwan Univ Intelligent Syst Res Inst POB 15000 Seoul 440746 South Korea

ISBN: (纸本)9783030013707;9783030013691

Cross-domain image to image translation task aims at learning the joint distribution of images from marginal distributions in their respective domains. However, estimation of joint distribution from marginal distribution is a challenging problem as there is no, one to one correspondence. To address this problem, we propose a general approach based on variational autoencoders along with latent space association network (VAE-LSAN). The variational autoencoders learn the marginal distribution of the images in the individual domain whereas, the association network provides the correspondence between the marginal distributions of the cross domains. Our architecture effectively performs mapping of images in individual as well as cross domains. Experimental results show state of the art performance of our framework on different datasets in term of synthesizing images within its respective domain as well as cross domains.

关键词： Generative models variational autoencoder Association network

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Exploiting Internal Randomness for Privacy in Vertical Federated Learning 29th

Exploiting Internal Randomness for Privacy in Vertical Feder...

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29th European Symposium on Research in Computer Security (ESORICS)

作者： Sun, Yulian Duan, Li Mendes, Ricardo Zhu, Derui Xia, Yue Li, Yong Fischer, Asja Huawei Technol Dusseldorf Dusseldorf Germany Paderborn Univ Paderborn Germany Ruhr Univ Bochum Bochum Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9783031708893;9783031708909

Vertical Federated Learning (VFL) is becoming a standard collaborative learning paradigm with various practical applications. Randomness is essential to enhancing privacy in VFL, but introducing too much external randomness often leads to an intolerable performance loss. Instead, as it was demonstrated for other federated learning settings, leveraging internal randomness - as provided by variational autoencoders (VAEs) -can be beneficial. However, the resulting privacy has never been quantified so far, nor has the approach been investigated for VFL. We therefore propose a novel differential privacy (DP) estimate, denoted as distance-based empirical local differential privacy (dELDP). It allows us to empirically bound DP parameters of models or model components, quantifying the internal randomness with appropriate distance and sensitivity metrics. We apply dELDP to investigate the DP of VAEs and observe values up to epsilon approximate to 6.4 and delta = 2(-32). Based on this, to link the dELDP parameters to the privacy of VAE-including VFL systems in practice, we conduct comprehensive experiments on the robustness against state-of-the-art privacy attacks. The results illustrate that the VAE system is robust against feature reconstruction attacks and outperforms other privacy-enhancing methods for VFL, especially when the adversary holds 75% of the features during label inference attacks.

关键词： privacy vertical federated learning distance-based empirical differential privacy variational autoencoder

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MANIFOLD LEARNING-SUPPORTED ESTIMATION OF RELATIVE TRANSFER FUNCTIONS FOR SPATIAL FILTERING 47

MANIFOLD LEARNING-SUPPORTED ESTIMATION OF RELATIVE TRANSFER ...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Brendel, Andreas Zeitler, Johannes Kellermann, Walter Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781665405409

Many spatial filtering algorithms used for voice capture in, e.g., tele-conferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for tele-conferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train variational autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method.

关键词： Manifold learning variational autoencoder relative transfer function spatial filtering unsupervised learning

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Automatic Feature Extraction of Channel Gains of Wireless Body Area Network using Convolutional Neural Networks 14

Automatic Feature Extraction of Channel Gains of Wireless Bo...

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14th International Symposium on Medical Information Communication Technology (ISMICT)

作者： Sano, Shintaro Aoyagi, Takahiro Tokyo Inst Technol Sch Engn Tokyo Japan

ISBN: (纸本)9781728166179

The channels of wireless body area networks (WBANs) are affected by human motion. Focusing on this characteristic of the WBAN channel, human motion classification and transmission power control have been investigated. Feature extraction of the WBAN channels is an important process for human motion classification. It is desirable that feature extraction is determined automatically. This is because it's difficult to select appropriate features by hand considering various factors affecting to the WBAN channels such as positions of transceivers, antennas, surrounding environment, etc. In this paper, an automatic feature extraction of the WBAN channel gains using convolutional neural networks (CNNs) is investigated. First, a human motion classifier is constructed using CNN. The accuracy rate of the classifier is evaluated and the relationship between the vector extracted by CNN and the features used in previous research is examined. Next, Feature extraction of the channel gains using variational autoencoders (VAEs) is performed. The relationship between latent variables extracted by VAE and human motion is examined. Through these considerations, an automatic feature extraction of the WBAN channel gains based on CNN is shown.

关键词： wireless body area network human motion classification feature extraction convolutional neural network variational autoencoder

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Comparative Analysis of Machine Learning Techniques for Island Heightmap Generation

Comparative Analysis of Machine Learning Techniques for Isla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Demergis, Dimitri Rowan Univ Coll Sci & Math Glassboro NJ 08028 USA

ISBN: (纸本)9780738133669

Traditional approaches to terrain heightmap generation rely on geometric methods to generate a matrix of elevation values using noise functions. More advanced approaches attempt to model the natural processes that shape landmasses in the real world, such as wind, moisture, and rainfall. This survey leverages recent advancements in image generation using generative machine learning models in order to evaluate their effectiveness in this problem space. A variational autoencoder (VAE), generative adversarial network (GAN), and PixelCNN network are trained on real-world island heightmap data and produce realistic island terrain. The author compares the results in terms of image quality and "closeness" to the original real images, and evaluates the trade-off between quality and training/generation speed.

关键词： machine learning generative models deep learning variational autoencoder generative adversarial network PixelCNN PixelRNN terrain generation landscape generation heightmap image synthesis

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Nonparallel Emotional Speech Conversion Using VAE-GAN 21

Nonparallel Emotional Speech Conversion Using VAE-GAN

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Interspeech Conference

作者： Cao, Yuexin Liu, Zhengchen Chen, Minchuan Ma, Jun Wang, Shaojun Xiao, Jing Ping An Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781713820697

This paper proposes a nonparallel emotional speech conversion (ESC) method based on variational autoencoder-Generative Adversarial Network (VAE-GAN). Emotional speech conversion aims at transforming speech from one source emotion to that of a target emotion without changing the speaker's identity and linguistic content. In this work, an encoder is trained to elicit the content-related representations from acoustic features. Emotion-related representations are extracted in a supervised manner. Then the transformation between emotion-related representations from different domains is learned using an improved cycle-consistent Generative Adversarial Network (CycleGAN). Finally, emotion conversion is performed by eliciting and recombining the content-related representations of the source speech and the emotion-related representations of the target emotion. Subjective evaluation experiments are conducted and the results show that the proposed method outperforms the baseline in terms of voice quality and emotion conversion ability.

关键词： Emotional Speech Conversion variational autoencoder Generative Adversarial Network Supervised Learning Style Transfer

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