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检索条件"主题词=Variational autoencoder"

共 1554 条记录，以下是401-410 订阅

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MULTI-SPEAKER AND MULTI-DOMAIN EMOTIONAL VOICE CONVERSION USING FACTORIZED HIERARCHICAL variational autoencoder

MULTI-SPEAKER AND MULTI-DOMAIN EMOTIONAL VOICE CONVERSION US...

引用

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

作者： Elgaar, Mohamed Park, Jungbae Lee, Sang Wan Korea Adv Inst Sci & Technol Daejeon South Korea Humelo Inc Daejeon South Korea KAIST Inst Artificial Intelligence Daejeon South Korea

ISBN: (纸本)9781509066315

Due to the complexity of emotional features, there has been limited success in emotional voice conversion. One major challenge is that conversion between more than two kinds of emotions often accompanies distortion of voice signal. The factorized hierarchical variational autoencoder (FHVAE) [1] was previously shown to have an ability, called sequence-level regularization, to generate disentangled representations of both sequence-level (such as speaker identity) and segment-level features. This study exploits the FHVAE pipeline to produce disentangled representations of emotion, making it possible to greatly facilitate emotional voice conversion. We propose three versions of algorithms for improving the quality of disentangled representation and audio synthesis. We conducted three mean opinion score (MOS) surveys to assess the performance of our models in terms of 1) speaker's voice preservation, 2) emotion conversion, and 3) audio naturalness.

关键词： Emotional Voice Conversion variational autoencoder Disentangled Representation Style Transfer

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VAEPP: variational autoencoder with a Pull-Back Prior 27th

VAEPP: Variational Autoencoder with a Pull-Back Prior

引用

27th International Conference on Neural Information Processing

作者： Chen, Wenxiao Liu, Wenda Cai, Zhenting Xu, Haowen Pei, Dan Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China

ISBN: (纸本)9783030638351;9783030638368

Many approaches to training generative models by distinct training objectives have been proposed in the past. variational autoencoder (VAE) is an outstanding model of them based on log-likelihood. In this paper, we propose a novel learnable prior, Pull-back Prior, for VAEs by adjusting the density of the prior through a discriminator that can assess the quality of data. It involves the discriminator from the theory of GANs to enrich the prior in VAEs. Based on it, we propose a more general framework, VAE with a Pull-back Prior (VAEPP), which uses existing techniques of VAEs and WGANs, to improve the log-likelihood, quality of sampling and stability of training. In MNIST and CIFAR-10, the log-likelihood of VAEPP outperforms models without autoregressive components and is comparable to autoregressive models. In MNIST, Fashion-MNIST, CIFAR-10 and CelebA, the FID of VAEPP is comparable to GANs and SOTA of VAEs.

关键词： variational autoencoder Deep generative model Adversarial training

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Laughter synthesis: A comparison between variational autoencoder and autoencoder 5

Laughter synthesis: A comparison between Variational autoenc...

引用

5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Mansouri, Nadia Lachiri, Zied Tunis El Manar Univ Signal Image & Informat Technol Res Lab LR SITI Natl Engn Sch Tunis ENIT Tunis Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175133

Laughter is one of the most famous non verbal sounds that human produce since birth, it conveys messages about our emotional state. These characteristics make it an important sound that should be studied in order to improve the human-machine interactions. In this paper we investigate the audio laughter generation process from its acoustic features. This suggested process is considered as an analysis-transformation-synthesis benchmark based on unsupervised dimensionality reduction techniques: The standard autoencoder (AE) and the variational autoencoder (VAE). Therefore, the laughter synthesis methodology consists of transforming the extracted high-dimensional log magnitude spectrogram into a low-dimensional latent vector. This latent vector contains the most valuable information used to reconstruct a synthetic magnitude spectrogram that will be passed through a specific vocoder to generate the laughter waveform. We systematically, exploit the VAE to create new sound (speech-laugh) based on the interpolation process. To evaluate the performance of these models two evaluation metrics were conducted: objective and subjective evaluations.

关键词： Laughter audio generation variational autoencoder autoencoder interpolation objective and subjective evaluation

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Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved variational autoencoder

Unsupervised Deep Learning for Fault Detection on Spacecraft...

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Chinese Automation Congress (CAC)

作者： Xiang, Gang Tao, Ran Peng, Yu Tian, Kun Qu, Chen Beihang Univ Natl Key Lab Aerosp Intelligent Control Beijing Aerosp Automat Control Inst Sch Automat Sci & Elect Engn Beijing Peoples R China Beijing Aerosp Automat Control Inst Natl Key Lab Aerosp Intelligent Control Beijing Peoples R China

ISBN: (纸本)9781728176871

Fault detection is important for improving the reliability of spacecraft, ensuring the long-term stable operation, and reducing the economic loss caused by failure. In order to solve the problems such as the large amount of test data, the scarcity of fault data samples and the real-time requirements in the field of spacecraft fault detection, an improved unsuperyised deep learning algorithm based on variational autoencoder (VAE) is proposed. The algorithm adopts Gated Recurrent Unit (GRU) based recurrent neural networks as encoder to automatically extract features of input data, and then uses VAE to learn the correlation features of multiple test data. The proposed network, trained only on the normal training dataset, is a typical unsupervised method which could learn features and reconstruct the data on the training set with a small loss. Once the reconstruction loss of the input data is larger than the pre-set threshold, the corresponding input data is considered as fault data. Experiments show that the proposed method is feasible and can effectively detect faults.

关键词： fault detection gated recurrent unit variational autoencoder deep learning spacecraft

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Anomaly Detection using variational autoencoder with Spectrum Analysis for Time Series Data 9

Anomaly Detection using Variational Autoencoder with Spectru...

引用

Joint 9th International Conference on Informatics, Electronics and Vision (ICIEV) / 4th International Conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Yokkampon, Umaporn Chumkamon, Sakmongkon Mowshowitz, Abbe Hayashi, Eiji Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka Japan CUNY City Coll Dept Comp Sci New York NY USA

ISBN: (纸本)9781728193311

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on variational autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining variational autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining variational autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.

关键词： Anomaly Detection variational autoencoder Time Series Data

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DEEP variational autoencoder FOR MODELING FUNCTIONAL BRAIN NETWORKS AND ADHD IDENTIFICATION 17

DEEP VARIATIONAL AUTOENCODER FOR MODELING FUNCTIONAL BRAIN N...

引用

IEEE 17th International Symposium on Biomedical Imaging (ISBI)

作者： Qiang, Ning Dong, Qinglin Sun, Yifei Ge, Bao Liu, Tianming Shaanxi Normal Univ Sch Phys & Informat Technol Xian Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781538693308

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain networks (FBNs). Recently, it has already been proven that deep learning can be applied on fMRI data with superb representation power over traditional machine learning methods. Limited by the high-dimension of fMRI volumes, deep learning suffers from the lack of data and overfitting. Generative models are known to have intrinsic ability of modeling small dataset and a Deep variational autoencoder (DVAE) is proposed in this work to tackle the challenge of insufficient data and incomplete supervision. The FBNs learned from fMRI were examined to be interpretable and meaningful and it was proven that DVAE has better performance on neuroimaging dataset over traditional models. With an evaluation on ADHD-200 dataset, DVAE performed excellent on classification accuracies on 4 sites.

关键词： Resting-state fMRI variational autoencoder ADHD Deep Learning Generative Learning

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A variational autoencoder-Based Secure Transceiver Design Using Deep Learning

A Variational Autoencoder-Based Secure Transceiver Design Us...

引用

IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Lin, Chia-Hung Wu, Chao-Chin Chen, Kuan-Fu Lee, Ta-Sung Natl Chiao Tung Univ Inst Commun Engn Hsinchu Taiwan Natl Chiao Tung Univ Ctr mmWave Smart Radar Syst & Technol Hsinchu Taiwan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728182988

To achieve new applications for 5G communications, physical layer security has recently drawn significant attention. In a wiretap channel system, our goal is to minimize information leakage to an eavesdropper while maximizing the performance of transmission to the desired or legitimate receiver. Complicated systems or channel models make it difficult to design secrecy systems based on the information theory. In this paper, we propose a deep learning-based transceiver design for secrecy systems as an alternative. Specifically, we modify the loss function design of a variational autoencoder, which is a special type of neural network, making it possible to provide both robust data transmission and security in an unsupervised fashion. We further investigate the impact of an imperfect channel state information and use simulation results to prove that our approach can outperform the existing learning-based methods.

关键词： physical layer security wiretap channel deep learning neural networks variational autoencoder

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Transfer Learning Approach for Botnet Detection Based on Recurrent variational autoencoder 3

Transfer Learning Approach for Botnet Detection Based on Rec...

引用

3rd International Workshop on Systems and Network Telemetry and Analytics (SNTA)

作者： Kim, Jeeyung Sim, Alex Kim, Jinoh Wu, Kesheng Hahm, Jaegyoon Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Texas A&M Univ Commerce TX USA KISTI Daejeon South Korea

ISBN: (纸本)9781450379809

Machine Learning (ML) methods have been widely used in Intrusion Detection Systems (IDS). In particular, many botnet detection methods are based on ML. However, due to the fast-evolving nature of network security threats, it is necessary to frequently retrain the ML tools with up-to-date data, especially because data labeling takes a long time and requires a lot of effort, making it difficult to generate training data. We propose transfer learning as a more effective approach for botnet detection, as it can learn from well curated source data and transfer the knowledge to a target problem domain not seen before. We devise an approach that is effective regardless whether or not the data from the target domain is labeled. More specifically, we train a neural network with the Recurrrent Variation autoencoder (RVAE) structure on the source data, and use RVAE to compute anomaly scores for data records from the target domain. In an evaluation of this transfer learning framework, we use CTU-13 dataset as a source domain and a fresh set of network monitoring data as a target domain. Tests show that the proposed transfer learning method is able to detect botnets better than semi-supervised learning method that was trained on the target domain data. The area under Receiver Operating Characteristic is 0.810 for transfer learning, and 0.779 for directly using RVAE on the target domain data.

关键词： botnet detection transfer learning Recurrent Neural Network variational autoencoder

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High-Dimensional Similarity Search with Quantum-Assisted variational autoencoder 20

High-Dimensional Similarity Search with Quantum-Assisted Var...

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Gao, Nicholas Wilson, Max Vandal, Thomas Vinci, Walter Nemani, Ramakrishna Rieffel, Eleanor NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA NASA Ames Res Ctr BAER Inst Moffett Field CA 94035 USA NASA Ames Res Ctr Moffett Field CA 94035 USA KBR Inc Houston TX USA Univ Bristol Quantum Engn CDT Bristol Avon England

ISBN: (纸本)9781450379984

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning [1] is touted as a potential approach to demonstrate quantum advantage within both the gate-model [2, 3] and the adiabatic [4, 5] schemes. For instance, the Quantum-assisted variational autoencoder (QVAE) [6] has been proposed as a quantum enhancement to the discrete VAE [7]. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

关键词： Similarity Search Quantum Machine Learning variational autoencoder Earth Science

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Botnet Detection Using Recurrent variational autoencoder

Botnet Detection Using Recurrent Variational Autoencoder

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Kim, Jeeyung Sim, Alex Kim, Jinoh Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Texas A&M Univ Commerce TX USA

ISBN: (纸本)9781728182988

Botnet detection is an active research topic as botnets are a source of many malicious activities, including distributed denial-of-service (DDoS), click-fraud, spamming, and crypto-mining attacks. However, it is getting more complicated to identify botnets due to the continuous evolution of botnet software and families that harness new types of devices and attack vectors. Recent studies employing machine learning (ML) showed improved performance to detect botnets to some extent, but they are still limited and ineffective with the lack of sequential pattern analysis, which is a key to detect various classes of botnets. In this paper, we propose a novel botnet detection method, built upon Recurrent variational autoencoder (RVAE), that effectively captures sequential characteristics of botnet anomalies. We validate the feasibility of the proposed method with the CTU-13 dataset that have been widely employed for botnet detection studies, and show that our method is at least comparable to existing techniques in terms of detection accuracy. In addition, our experimental results show that the proposed method can detect previously unseen botnets by utilizing sequential patterns of network traffic. We will also show how our method can detect botnets in the streaming mode, which is the essential requirement to perform real-time, on-line detection.

关键词： botnet detection anomaly scoring Recurrent Neural Network variational autoencoder network security

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