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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是241-250 订阅

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Imputation of Missing Values in Training Data using variational autoencoder 39

Imputation of Missing Values in Training Data using Variatio...

引用

IEEE 39th International Conference on Data Engineering (ICDE)

作者： Hong, Xuerui Hao, Shuang Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China

ISBN: (纸本)9798350322446

Missing values in the training data must be handled before training the machine learning (ML) model. One widely used practice is to delete the data with missing values directly, but this will undoubtedly cause the loss of information and thus affect the model's accuracy. Missing values can also be estimated using extensively studied statistical and ML-based imputation algorithms. The emergence of deep generative models also opens up new opportunities, especially for dealing with a particularly large number of missing values. However, none of these methods aim to improve further the utility of imputed training data for the downstream target task. In this paper, we propose VAIM, a variational autoencoder (VAE) based framework for imputing missing values in training data. VAIM contains three modules: VAE works as a generator to guess the value of missing data;a discriminator tries to distinguish the original observable data from the data imputed by VAE;a learner is trained to guarantee the utility of imputed training data in the downstream classification task. We also introduce the self-attention mechanism in VAE, which allows the model to focus on more relevant information when imputing the missing values. Experiments on multiple datasets with different missing rates and different missing types show that our method performs better than state-of-the-art approaches.

关键词： missing value imputation variational autoencoder self-attention mechanism meta-learning paradigm

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Underwater Passive Target Classification based on β variational autoencoder and MFCC 12

Underwater Passive Target Classification based on β Variati...

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12th Sensor Signal Processing for Defence Conference (SSPD)

作者： Sunilkumar, Adarsh Joseph, Shamju K. Kumar, Manoj K. DRDO Naval Phys & Oceanog Lab Kochi India

ISBN: (纸本)9798350337327

Underwater passive target classification is an open set classification problem, where quite often test data of those classes, which were not present during training phase is encountered and it is a challenging task due to the intrinsic complexity of the radiated noise from the target. Conventional classification architectures with spectral processing often fail miserably. Supervised learning methods like deep learning, offers higher success rate but they require enormous amount of data for training and their performance in open set classification is again a challenge. This paper presents an effective method for underwater target classification by the beta variational autoencoder (beta - VAE) model with Mel Frequency Cepstral Coefficients (MFCCs) features. MFCC effectively utilises the non-linear auditory effect of the human ear with different frequencies. beta - VAE, being one of the generative models, is capable of generalizing with less amount of data. Classification experiments on various underwater targets have been performed with the proposed method, and results indicate that the proposed method is effective in underwater passive target classification.

关键词： Open Set Classification Supervised Learning Deep Learning variational autoencoder Mel Frequency Cepstral Coefficients

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Complex-Valued variational autoencoder: A Novel Deep Generative Model for Direct Representation of Complex Spectra 21

Complex-Valued Variational Autoencoder: A Novel Deep Generat...

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Interspeech Conference

作者： Nakashika, Toru Univ Electrocommun Chofu Tokyo Japan

ISBN: (纸本)9781713820697

In recent years, variational autoencoders (VAEs) have been attracting interest for many applications and generative tasks. Although the VAE is one of the most powerful deep generative models, it still has difficulty representing complex-valued data such as the complex spectra of speech. In speech synthesis, we usually use the VAE to encode Mel-cepstra, or raw amplitude spectra, from a speech signal into normally distributed latent features and then synthesize the speech from the reconstruction by using the Griffin-Lim algorithm or other vocoders. Such inputs are originally calculated from complex spectra but lack the phase information, which leads to degradation when recovering speech. In this work, we propose a novel generative model to directly encode the complex spectra by extending the conventional VAE. The proposed model, which we call the complex-valued VAE (CVAE), consists of two complex-valued neural networks (CVNNs) of an encoder and a decoder. In the CVAE, not only the inputs and the parameters of the encoder and decoder but also the latent features are defined as complex-valued to preserve the phase information throughout the network. The results of our speech encoding experiments demonstrated the effectiveness of the CVAE compared to the conventional VAE in both objective and subjective criteria.

关键词： complex neural networks deep learning variational autoencoder speech synthesis speech encoding

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Object Tracking of Aerial Imaging Device Image Using variational autoencoder and External Memory 13

Object Tracking of Aerial Imaging Device Image Using Variati...

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13th International Conference on Ubiquitous and Future Networks (ICUFN)

作者： Park, Keunho Kim, Byoungjun Kim, Donghoon Kim, Seon-Hyeong Kim, Seo-jeong Jeong, Sunghwan Korea Elect Technol Inst Jeonju Si South Korea

ISBN: (纸本)9781665485500

Object tracking is a fundamental problem in the field of computer vision. The object tracking methods proposed so far can be divided into a 'discriminative correlation filter' and a 'deep learning' based methods with a complex structure and a lot of computation. In this paper, we propose an algorithm for tracking objects with a simple structure while maintaining tracking performance using a convolutional variational auto-encoder, external memory, and a Siamese network. As a result of an experiment with an RT (real-time) data set to measure real-time, the result was a precision of 0.546 and a success rate of 0.527.

关键词： deep learning object tracking variational autoencoder external memory Siamese network

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Re-balancing variational autoencoder Loss for Molecule Sequence Generation 20

Re-balancing Variational Autoencoder Loss for Molecule Seque...

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11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Yan, Chaochao Wang, Sheng Yang, Jinyu Xu, Tingyang Huang, Junzhou Univ Texas Arlington Arlington TX 76019 USA Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781450379649

Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previouswork, we encode molecules into continuous vectors in the latent space and then decode the embedding vectors into molecules under the variational autoencoder (VAE) framework. We investigate the posterior collapse problem of the current widely-used RNN-based VAEs for the molecule sequence generation. For the first time, we point out that the underestimated reconstruction loss of VAEs leads to the posterior collapse, and we also provide both analytical and experimental evidences to support our findings. To fix the problem and avoid the posterior collapse, we propose an effective and efficient solution in this work. Without bells and whistles, our method achieves the state-of-the-art reconstruction accuracy and competitive validity score on the ZINC 250K dataset. When generating 10,000 unique valid molecule sequences from the random prior sampling, it costs the JT-VAE 1450 seconds while our method only needs 9 seconds on a regular desktop machine.

关键词： molecule generation variational autoencoder recurrent neural networks posterior collapse

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RVAE-ABFA : Robust Anomaly Detection for High Dimensional Data Using variational autoencoder 44

RVAE-ABFA : Robust Anomaly Detection for High Dimensional Da...

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Gao, Yuda Shi, Bin Dong, Bo Chen, Yan Mi, Lingyun Huang, Zhiping Shi, Yuanyuan Xi An Jiao Tong Univ Sch Comp Sci & Technol SPKLSTN Lab Xian Peoples R China Xi An Jiao Tong Univ Sch Distance Educ Natl Engn Lab Big Data Analyt Xian Peoples R China SERVYOU Grp Hangzhou Peoples R China

ISBN: (纸本)9781728173030

The curse of dimensionality is a fundamental difficulty in anomaly detection for high dimensional data. To deal with this problem, the autoencoder based approach is an elegant solution. However, existing works require a clean training dataset that is not always guaranteed in real scenarios. In this paper, we propose a novel anomaly detection method named RVAE-ABFA (robust variational autoencoder with attention based feature adaptation for high dimensional data anomaly detection), which significantly improves the anomaly detection performance when training data is contaminated. Rather than only utilize reconstruction error, we take the learned low dimensional embeddings generated by variational autoencoder into consideration. In RVAE-ABFA, the learned low dimensional embeddings are helpful to detect anomalies in contaminated data because of the ability of variational inference. We also propose an ABFA (attention based feature adaptation) mechanism to adjust the weights of low dimensional embeddings and reconstruction error. Furthermore, we adopt the adversarial training criterion to perform variational inference by the adversarial network named RAAE-ABFA (robust adversarial autoencoder with attention based feature adaptation for high dimensional data anomaly detection) in which we can generate extra samples when training data is not enough. Experimental results on several benchmark datasets show that the proposed method significantly outperforms state-of-the-art unsupervised anomaly detection methods and is more robust when training data is contaminated.

关键词： Adversarial Neural Network Anomaly Detection Robust Learning variational autoencoder

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Refined WaveNet Vocoder for variational autoencoder Based Voice Conversion 27

Refined WaveNet Vocoder for Variational Autoencoder Based Vo...

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27th European Signal Processing Conference (EUSIPCO)

作者： Huang, Wen-Chin Wu, Yi-Chiao Hwang, Hsin-Te Tobing, Patrick Lumban Hayashi, Tomoki Kobayashi, Kazuhiro Toda, Tomoki Tsao, Yu Wang, Hsin-Min Acad Sinica Taipei Taiwan Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9789082797039

This paper presents a refinement framework of WaveNet vocoders for variational autoencoder (VAE) based voice conversion (VC), which reduces the quality distortion caused by the mismatch between the training data and testing data. Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation. In this work, we take advantage of the particular structure of VAEs to refine WaveNet vocoders with the self-reconstructed features generated by VAE, which are of similar characteristics with the converted features while having the same temporal structure with the target natural features. We analyze these features and show that the self-reconstructed features are similar to the converted features. Objective and subjective experimental results demonstrate the effectiveness of our proposed framework.

关键词： voice conversion variational autoencoder WaveNet vocoder speaker adaptation

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Black-Box Audio Adversarial Example Generation Using variational autoencoder 23rd

Black-Box Audio Adversarial Example Generation Using Variati...

引用

23rd International Conference on Information and Communications Security (ICICS)

作者： Zong, Wei Chow, Yang-Wai Susilo, Willy Univ Wollongong Sch Comp & Informat Technol Inst Cybersecur & Cryptol Wollongong NSW Australia

ISBN: (纸本)9783030880521;9783030880514

Automatic speech recognition (ASR) applications are ubiquitous these days. A variety of commercial products utilize powerful ASR capabilities to transcribe user speech. However, as with other deep learning models, the techniques underlying ASR models suffer from adversarial example (AE) attacks. Audio AEs resemble non-suspicious audio to the casual listener, but will be incorrectly transcribed by an ASR system. Existing black-box AE techniques require excessive requests sent to a targeted system. Such suspicious behavior can potentially trigger a threat alert on the system. This paper proposes a method of generating black-box AEs in a way that significantly reduces the required amount of requests. We describe our proposed method and presents experimental results demonstrating its effectiveness in generating word-level and sentence-level AEs that are incorrectly transcribed by an ASR system.

关键词： Adversarial example Automatic speech recognition Deep learning Machine learning variational autoencoder

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A NOVEL variational autoencoder BASED RADAR SIGNAL RECONSTRUCTION ALGORITHM USING POLLUTED DATA

A NOVEL VARIATIONAL AUTOENCODER BASED RADAR SIGNAL RECONSTRU...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Jing, Zehuan Wu, Bin Li, Peng Yang, Rui Li, Jingyi Wang, Zhao Xidian Univ Sch Elect Engn Minist Educ Key Lab Elect Informat Countermeasure & Simulat T Xian Shaanxi Peoples R China

ISBN: (纸本)9781728163741

In the transmission process, radar signal could be polluted easily, and the radar individual recognition process will be undermined by the polluted data. Some existing algorithms so far use the image denoising method to deal with this issue while ignoring the essential characteristics of the signal, and thus the noise reduction effect is poor. To address this issue, a novel variational autoencoder based radar signal reconstruction algorithm is proposed in this paper to reconstruct high quality data from the polluted ones by compressing the one-dimensional polluted signal and learning the essential characteristics. The experiments prove that the algorithm indeed improves the quality of the reconstructed data compared with the polluted signal.

关键词： Radar individual recognization Deep Neural Network variational autoencoder Noise reduction

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A Method to Inspect the Implementation of Electricity Price Based on Deep Learning variational autoencoder 2

A Method to Inspect the Implementation of Electricity Price ...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Gao, Xiying Ye, Ning Song, Jinchun Wang, Haomiao Zhang, Ye Guan, Yan Song, Xiaowen Cai, Yingkai Zhang, Wenshu Hui, Qian Li, Dan State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Mkt Dept Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China State Grid Liaoyang Elect Power B&E Off Liaoyang Peoples R China

ISBN: (纸本)9781538685495

In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational autoencoder based anomaly detection algorithm(VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.

关键词： deep learning variational autoencoder electricity price inspection implementation data heterogeneity

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