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检索条件"主题词=Variational autoencoder"

共 1537 条记录，以下是221-230 订阅

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TTS-by-TTS 2: Data-Selective Augmentation for Neural Speech Synthesis Using Ranking Support Vector Machine with variational autoencoder 23

TTS-by-TTS 2: Data-Selective Augmentation for Neural Speech ...

引用

Interspeech Conference

作者： Song, Eunwoo Yamamoto, Ryuichi Kwon, Ohsung Song, Chan-Ho Hwang, Min-Jae Oh, Suhyeon Yoon, Hyun-Wook Kim, Jin-Seob Kim, Jae-Min NAVER Corp Seongnam South Korea LINE Corp Tokyo Japan

Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic data is not always advantageous for improving training efficiency. Our aim in this study is to selectively choose synthetic data that are beneficial to the training process. In the proposed method, we first adopt a variational autoencoder whose posterior distribution is utilized to extract latent features representing acoustic similarity between the recorded and synthetic corpora. By using those learned features, we then train a ranking support vector machine (RankSVM) that is well known for effectively ranking relative attributes among binary classes. By setting the recorded and synthetic ones as two opposite classes, RankSVM is used to determine how the synthesized speech is acoustically similar to the recorded data. Then, synthetic TTS data, whose distribution is close to the recorded data, are selected from large-scale synthetic corpora. By using these data for retraining the TTS model, the synthetic quality can be significantly improved. Objective and subjective evaluation results show the superiority of the proposed method over the conventional methods.

关键词： Speech synthesis data augmentation variational autoencoder ranking support vector machine

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Bike-Share Demand Prediction using Attention based Sequence to Sequence and Conditional variational autoencoder 3

Bike-Share Demand Prediction using Attention based Sequence ...

引用

3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility (PredictGIS)

作者： Mimura, Tomohiro Ishiguro, Shin Kawasaki, Satoshi Fukazawa, Yusuke NTT DOCOMO INC Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781450369640

In recent years, bicycle sharing services (bike-shares) have been established worldwide. One important aspect of bike-share management is to periodically rebalance the positions of the available bikes. Because the bike demand varies by and over time, the number of bikes at each bike-port tends to become unbalanced. To efficiently rebalance a bike-share system, it is essential to predicting the number of bikes in each bike-port. In this paper, we propose a method to predicting bike demand and the number of bike pickups and drop offs at each bike-port every hour, up to 24 hours in advance. To predict demand, we used a time series generation model based on the variational autoencoders model and the Attention based Sequence to Sequence learning model. We named this method "Conditional variational autoencoders considering Partial Future data " (CVAE-PF). In the experiment, our proposed method showed higher prediction accuracy in root mean square error (RMSE) compared to conventional methods.

关键词： bike-share demand prediction neural networks variational autoencoder real-time statistics population

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DISENTANGLED SPEECH REPRESENTATION LEARNING BASED ON FACTORIZED HIERARCHICAL variational autoencoder WITH SELF-SUPERVISED OBJECTIVE 31

DISENTANGLED SPEECH REPRESENTATION LEARNING BASED ON FACTORI...

引用

IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Xie, Yuying Arildsen, Thomas Tan, Zheng-Hua Aalborg Univ Dept Elect Syst Aalborg Denmark

ISBN: (纸本)9781728163383

Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) presents a way to disentangle a speech signal into sequential-level and segmental-level features, which represent speaker identity and speech content information, respectively. As a self-supervised objective, autoregressive predictive coding (APC), on the other hand, has been used in extracting meaningful and transferable speech features for multiple downstream tasks. Inspired by the success of these two representation learning methods, this paper proposes to integrate the APC objective into the FHVAE framework aiming at benefiting from the additional self-supervision target. The main proposed method requires neither more training data nor more computational cost at test time, but obtains improved meaningful representations while maintaining disentanglement. The experiments were conducted on the TIMIT dataset. Results demonstrate that FHVAE equipped with the additional self-supervised objective is able to learn features providing superior performance for tasks including speech recognition and speaker recognition. Furthermore, voice conversion, as one application of disentangled representation learning, has been applied and evaluated. The results show performance similar to baseline of the new framework on voice conversion.

关键词： Disentangled representation learning variational autoencoder autoregressive predictive coding

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Learning an Optimisable Semantic Segmentation Map with Image Conditioned variational autoencoder 20th

Learning an Optimisable Semantic Segmentation Map with Image...

引用

20th International Conference on Image Analysis and Processing (ICIAP)

作者： Zhuang, Pengcheng Sekikawa, Yusuke Hara, Kosuke Saito, Hideo Keio Univ Grad Sch Sci & Technol Yokohama Kanagawa 2238522 Japan Denso IT Lab Tokyo 1500002 Japan

ISBN: (纸本)9783030306458;9783030306441

Recent semantic segmentation systems have achieved significant improvement by performing pixel-wise training with hierarchical features using deep convolutional neural network models. While the learning process usually requires pixel-level annotated images, it is difficult to get desirable amounts of fine-labeled data and thus the training set size is more likely to be limited, often in thousands. This means that top methods for a dataset can be fine-tuned for a specific situation, making the generalization ability unclear. In real-world applications like self-driving systems, ambiguous region or lack of context information can cause errors in the predicted results. Resolving such ambiguities is crucial for subsequent operations to be performed safely. We are inspired by work from CodeSLAM where optimizable pixel-wise depth representation is learned. We modify the regression method to work on the pixel-wise classification problem. By training a variational auto-encoder network conditioned with a color image, the computed latent space works as a low-dimensional representation of semantic segmentation, which can be efficiently optimized. As a consequence, our model can correct the error or ambiguity of the prediction during the inference phase given useful scene information. We show how this approach works by giving partial scene truth and perform optimization on the latent variable.

关键词： Semantic segmentation variational autoencoder Optimization

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VAEMax: Open-Set Intrusion Detection based on OpenMax and variational autoencoder 5

VAEMax: Open-Set Intrusion Detection based on OpenMax and Va...

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5th Information Communication Technologies Conference (ICTC)

作者： Qiu, Zhiyin Zhou, Ding Zhai, Yahui Liu, Bo He, Lei Cao, Jiuxin Purple Mt Labs Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China TravelSky Technol Ltd Beijing Peoples R China

ISBN: (纸本)9798350373530;9798350373523

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on system or equipment. This paper aims to develop an open-set intrusion detection model to classify known attacks as well as inferring unknown ones. To achieve this, we employ OpenMax and variational autoencoder to propose a dual detection model, VAEMax. First, we extract flow payload feature based on one-dimensional convolutional neural network. Then, the OpenMax is used to classify flows, during which some unknown attacks can be detected, while the rest are misclassified into a certain class of known flows. Finally, use VAE to perform secondary detection on each class of flows, and determine whether the flow is an unknown attack based on the reconstruction loss. Experiments performed on dataset CIC-IDS2017 and CSE-CIC-IDS2018 show our approach is better than baseline models and can be effectively applied to realistic network environments.

关键词： open-set intrusion detection OpenMax variational autoencoder

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Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a variational autoencoder 21

Generating and Blending Game Levels via Quality-Diversity in...

引用

16th International Conference on the Foundations of Digital Games (FDG)

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450384223

Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.

关键词： PCGML procedural content generation level generation quality diversity MAP-Elites variational autoencoder game blending

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HiCoVA: Hierarchical Conditional variational autoencoder for Keyphrase Generation 21

HiCoVA: Hierarchical Conditional Variational Autoencoder for...

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

作者： Santosh, T. Y. S. S. Reddy, Nikhil, V Anoop, V Sanyal, Debarshi Kumar Das, Partha Pratim Microsoft Hyderabad India IIT Kharagpur Kharagpur India Indian Assoc Cultivat Sci Kolkata India

ISBN: (纸本)9781450384469

The task of keyphrase generation, unlike extraction, aims to generate the phrases which succinctly capture the key information of the source text, that are even absent in the document (i.e., do not match any contiguous sub-sequence of source text). Despite the significant progress achieved by sequence-to-sequence (seq2seq) models in modelling such a high entropy task, they are limited by their deterministic modelling capability which limits the generation of a diverse set of keyphrases. To address the above limitation, in this paper, we propose to incorporate Conditional variational autoencoder (CoVA) into seq2seq models for its ability to represent a set of keyphrases as a probabilistic distribution, which improves the diversity of the generated keyphrases. We model the probabilistic distribution using a hierarchical latent structure where a global latent variable tries to model the diversity among the keyphrases and local latent variables control the generation of each keyphrase to make them coherent. Experimental results on four benchmark datasets of research papers demonstrate the effectiveness of our proposed approach in achieving a large improvement in diversity along with modest gains in quality with respect to previous models.

关键词： Keyphrase generation variational autoencoder diversity

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Fast variational autoencoder with Inverted Multi-Index for Collaborative Filtering 22

Fast Variational AutoEncoder with Inverted Multi-Index for C...

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31st ACM Web Conference (WWW)

作者： Chen, Jin Lian, Defu Jin, Binbin Huang, Xu Zheng, Kai Chen, Enhong Univ Elect Sci & Technol China Chengdu Peoples R China Uni Sci & Technol China Hefei Anhui Peoples R China Huawei Cloud Comp Technol Co Ltd Dongguan Peoples R China

ISBN: (纸本)9781450390965

variational autoencoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over all items, such that it takes linear costs in the number of items to compute the loss and gradient for optimization. This hinders the practical use due to millions of items in real-world scenarios. Importance sampling is an effective approximation method, based on which the sampled softmax has been derived. However, existing methods usually exploit the uniform or popularity sampler as proposal distributions, leading to a large bias of gradient estimation. To this end, we propose to decompose the inner-product-based softmax probability based on the inverted multi-index, leading to sublinear-time and highly accurate sampling. Based on the proposed proposals, we develop a fast variational autoencoder (FastVAE) for collaborative filtering. FastVAE can outperform the state-of-the-art baselines in terms of both sampling quality and efficiency according to the experiments on three real-world datasets.

关键词： Sampling Inverted Multi-Index Recommender Systems variational autoencoder Collaborative Filtering

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

A NOVEL VARIATIONAL AUTOENCODER BASED RADAR SIGNAL RECONSTRU...

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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 Unified Unsupervised Gaussian Mixture variational autoencoder for High Dimensional Outlier Detection

A Unified Unsupervised Gaussian Mixture Variational Autoenco...

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IEEE International Conference on Big Data (Big Data)

作者： Liao, Weixian Guo, Yifan Chen, Xuhui Li, Pan Towson Univ Dept Comp & Informat Sci Towson MD 20252 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

ISBN: (纸本)9781538650356

Paradigm-shifting systems such as cyber-physical systems, collect data of high-or ultrahigh-dimensionality tremendously. Detecting outliers in this type of systems provides indicative understanding in wide-ranging domains such as system health monitoring, information security, etc. Previous dimensionality reduction based outlier detection methods suffer from the incapability of well preserving the critical information in the low-dimensional latent space, mainly because they generally assume an isotropic Gaussian distribution as prior and fail to mine the intrinsic multimodality in high dimensional data. Moreover, most of the schemes decouple the model learning process, resulting in suboptimal performance. To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture variational autoencoder for outlier detection. Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture probabilities by the latent distribution and extracted features, which is further used by the Gaussian mixture model to estimate sample densities with the Expectation-Maximization ( EM) algorithm. The inference model is optimized jointly with the variational autoencoder, the deep brief network, and the Gaussian mixture model. Afterwards, the proposed detector identifies outliers when the estimated sample density exceeds a learned threshold. Extensive simulations on six public benchmark datasets show that the proposed framework outperforms state-of-the-art outlier detection schemes and achieves, on average, 27% improvements in F1 score.

关键词： Outlier detection Gaussian mixture model variational autoencoder

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