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检索条件"主题词=Conditional Variational Autoencoder"

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Topic-Aware Automatic Snippet Generation for Resolving Multiple Meaning on Web Search Result 9

Topic-Aware Automatic Snippet Generation for Resolving Multi...

引用

9th International Conference on Awareness Science and Technology (iCAST)

作者： Abe, Hiroyuki Matsuhara, Masafumi Chakraborty, Goutam Mabuchi, Hiroshi Iwate Prefectural Univ Grad Sch Software & Informat Sci Takizawa Iwate Japan Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Iwate Japan

ISBN: (纸本)9781538658260

In recent years, the amount of information on the Web is growing exponentially with the spread of the Internet. We generally use search engines to search for the intended information. However, the search engine displays the Web pages including the entered search query in list format. It is difficult for the user to find out the intended information if the entered search query is a word whose meaning depends on the situation and location of the user. It needs the intended information to the multiple hidden topics. In this research, we classify Web search results based on each topic. The topic is defined as the latent meaning, and the contents included in the word. Moreover, our method displays automatically generated snippets for each topic with the Web search results to the user. It is easy to find required information from snippets, even though the intended information is ambiguous. It first classifies the Web search results by Latent Dirichlet Allocation (LDA) which is a major topic model method. It then generates the snippets using conditional variational autoencoder (conditional VAE) based on the clustering of We search results. It is expected that using LDA for the clustering will group the Web search result according to the latent meanings of the search query. Also, we expect that proper snippets will be generated for each topic by conditional VAE. In this paper, we show that LDA is effective for the clustering of Web search results. Moreover, the snippets generated by conditional VAE is able to generate sentences considered each topic.

关键词： Information Retrieval Web Search Result Clustering Latent Dirichlet Allocation conditional variational autoencoder Long Short-Term Memory

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CVAE-Attention: CVAE based Semi-Supervised Sentiment Classification using Attention 19

CVAE-Attention: CVAE based Semi-Supervised Sentiment Classif...

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International Conference on Pattern Recognition and Artificial Intelligence (PRAI)

作者： Yu, Jifang Wu, Jiangqin Wei, Baogang Liu, Yuanyuan Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China

ISBN: (纸本)9781450372312

Text sentiment classification is an important domain in NLP, and the related technical research has been mature. The sentiment classification of text with the "but" contrastive marker is a challenging problem. In this paper, a semi-supervised framework based on conditional variational autoencoder using attention, called CVAE-Attention, is proposed for sentiment classification. In the CVAE-Attention framework, the attention mechanism is introduced to cope with the contrastive structure. The latent semantic information of the clause after "but" (but-clause) is extracted through the attention model, and is incorporated into the generative model to enlarge the effect of the but-clause. Experiments show that the proposed method is effective compared with other state-of-the-art semi-supervised methods.

关键词： conditional variational autoencoder Semi-supervised Learning Sentiment Classification Attention Mechanism

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Towards a system for automatic traffic sound event detection 28

Towards a system for automatic traffic sound event detection

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28th Telecommunications Forum (TELFOR)

作者： Chavdar, Marko Gerazov, Branislav Ivanovski, Zoran Kartalov, Tomislav ITEK Syst Gevgelija North Macedonia Ss Cyril & Methodius Univ Fac Elect Engn & Informat Technol Rugjer Boshkovikj 18 Skopje 1000 North Macedonia

ISBN: (纸本)9780738142432

Intelligent Traffic Surveillance systems have helped improve road safety through ensuring timely response to events such as traffic accidents and congestion. Our aim is to devise a robust system capable of traffic audio events detection in a real-life environment. At the core of this system is a deep learning model capable of detecting anomalous events and their classification based on their acoustic waveform. We present the results of a series of experiments designed to optimize the architecture of this model based on different algorithms for audio processing. The results show that the designed model has competitive performance to approaches published in literature.

关键词： Sound event detection ITS Traffic surveillance system conditional variational autoencoder Convolutional neural network

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Attributed Network Embedding with Community Preservation 7

Attributed Network Embedding with Community Preservation

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7th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Huang, Tong Zhou, Lihua Wang, Lizhen Du, Guowang Lu, Kevin Yunnan Univ Sch Informat Kunming Yunnan Peoples R China Brunel Univ Uxbridge UB8 3PH Middx England

ISBN: (纸本)9781728182063

Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a unified framework. In this study, we develop a deep neural network-based framework for Attributed Network Embedding with Community Preservation (ANECP), which simultaneously incorporates the topological structure, node attributes as well as community structure together to obtain the low-dimensional distributed representations of nodes in the network. The use of deep neural networks captures the underlying high non-linearity in both topology and attribute information, while the incorporation of the community structure resolves the issues of data sparsity from microscopic perspective. Consequently, the obtained node representations can preserve proximity and discriminative. We conducted experimental studies using six real-world datasets. The experimental results show that proposed ANECP has superior performance over the existing methods.

关键词： Network embedding topological structure node attribute community structure conditional variational autoencoder

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Reconstructing Spatiotemporal Data with C-VAEs 27th

Reconstructing Spatiotemporal Data with C-VAEs

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27th European Conference on Advances in Databases and Information Systems (ADBIS)

作者： Ribeirol, Tiago F. R. Silva, Fernando Costa, Rogerio Luis de C. Polytech Leiria ESTG CIIC Leiria Portugal Polytech Leiria CIIC Leiria Portugal

ISBN: (纸本)9783031429132;9783031429149

The continuous representation of spatiotemporal data commonly relies on using abstract data types, such as moving regions, to represent entities whose shape and position continuously change over time. Creating this representation from discrete snapshots of real-world entities requires using interpolation methods to compute in-between data representations and estimate the position and shape of the object of interest at arbitrary temporal points. Existing region interpolation methods often fail to generate smooth and realistic representations of a region's evolution. However, recent advancements in deep learning techniques have revealed the potential of deep models trained on discrete observations to capture spatiotemporal dependencies through implicit feature learning. In this work, we explore the capabilities of conditional variational autoencoder (C-VAE) models to generate smooth and realistic representations of the spatiotemporal evolution of moving regions. We evaluate our proposed approach on a sparsely annotated dataset on the burnt area of a forest fire. We apply compression operations to sample from the dataset and use the C-VAE model and other commonly used interpolation algorithms to generate in-between region representations. To evaluate the performance of the methods, we compare their interpolation results with manually annotated data and regions generated by a U-Net model. We also assess the quality of generated data considering temporal consistency metrics. The proposed C-VAE-based approach demonstrates competitive results in geometric similarity metrics. It also exhibits superior temporal consistency, suggesting that C-VAE models may be a viable alternative to modeling the spatiotemporal evolution of 2D moving regions.

关键词： Region Interpolation Problem Moving Regions conditional variational autoencoder Continuous Representation

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SECT: A Successively conditional Transformer for Controllable Paraphrase Generation

SECT: A Successively Conditional Transformer for Controllabl...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Xue, Tang Zhao, Yuran Yang, Chaoqi Liu, Gongshen Li, Xiaoyong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Illinois Dept Comp Sci Champaign IL USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Paraphrase generation has consistently been a challenging area in the field of NLP. Despite the considerable achievements made by previous work, existing methods lack a flexible way to include multiple controllable attributes to enhance the diversity of paraphrased sentences. To overcome this challenge, we propose a Successively conditional Transformer (SECT) to tackle this task. SECT is based on a combination of conditional variational autoencoder (CVAE) and Transformer framework to generate diversified words. More specifically, our SECT deploys multi-head attention and memory gate mechanism to keep the interaction between each of the attributes and the corresponding encoder layer hidden state. To address the problem of absorbing flexible attributes, we apply a successive structure to our SECT, which enables the framework to couple the CVAE latent variables with the encoder layer hidden states progressively. In addition, our SECT is trained by minimizing a tailor-designed loss for producing paraphrased sentences as required. Finally, we conduct extensive experiments to substantiate the validity and effectiveness of our proposed model. The results show that SECT significantly outperforms the existing state-of-the-art approaches and generates more diverse paraphrased sentences.

关键词： paraphrase generation conditional variational autoencoder transformer successive structure

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Flow-Grounded Spatial-Temporal Video Prediction from Still Images 15th

Flow-Grounded Spatial-Temporal Video Prediction from Still I...

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15th European Conference on Computer Vision (ECCV)

作者： Li, Yijun Fang, Chen Yang, Jimei Wang, Zhaowen Lu, Xin Yang, Ming-Hsuan Univ Calif Merced CA 95343 USA Adobe Res San Jose CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9783030012403;9783030012397

Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating consecutive multiple future frames by observing one single still image only. We formulate the multi-frame prediction task as a multiple time step flow (multi-flow) prediction phase followed by a flow-to-frame synthesis phase. The multi-flow prediction is modeled in a variational probabilistic manner with spatial-temporal relationships learned through 3D convolutions. The flow-to-frame synthesis is modeled as a generative process in order to keep the predicted results lying closer to the manifold shape of real video sequence. Such a two-phase design prevents the model from directly looking at the high-dimensional pixel space of the frame sequence and is demonstrated to be more effective in predicting better and diverse results. Extensive experimental results on videos with different types of motion show that the proposed algorithm performs favorably against existing methods in terms of quality, diversity and human perceptual evaluation.

关键词： Future prediction conditional variational autoencoder 3D convolutions

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Geometry Aligned variational Transformer for Image-conditioned Layout Generation 22

Geometry Aligned Variational Transformer for Image-condition...

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30th ACM International Conference on Multimedia (MM)

作者： Cao, Yunning Ma, Ye Zhou, Min Liu, Chuanbin Xie, Hongtao Ge, Tiezheng Jiang, Yuning Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Beijing Peoples R China

ISBN: (纸本)9781450392037

Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and the image. However, most previous methods simply focus on image-content-agnostic layout generation, without leveraging the complex visual information from the image. To this end, we explore a novel paradigm entitled image-conditioned layout generation, which aims to add text overlays to an image in a semantically coherent manner. Specifically, we propose an Image-Conditioned variational Transformer (ICVT) that autoregressively generates various layouts in an image. First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images. Subsequently, we take them as building blocks of conditional variational autoencoder (CVAE), which demonstrates appealing diversity. Second, in order to alleviate the gap between layout elements domain and visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. In addition, we construct a large-scale advertisement poster layout designing dataset with delicate layout and saliency map annotations. Experimental results show that our model can adaptively generate layouts in the non-intrusive area of the image, resulting in a harmonious layout design.

关键词： image-conditioned layout generation conditional variational autoencoder Transformer cross attention

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Style-driven Handwritten Character Generation 30

Style-driven Handwritten Character Generation

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Ucar, Ozdenur Genc, Yakup Gebze Tech Univ Bilgisayar Muhendisligi Bolumu Gebze Turkey

ISBN: (纸本)9781665450928

Handwritten character generation is a popular topic with a variety of applications. Many methods of character generation have been proposed in the literature, but few of these methods focus on preserving the writer's style while producing handwritten characters. Representing handwriting styles involves the challenge of representing both the style of each character and the overall style of the writer. In this study, unlike the studies in the literature, it is tried to produce the characters of the person that we have not seen yet by extracting the handwriting style of the person with limited data. While producing handwritten characters, spline curves of the characters were used as input, as well as handwritten images. In the developed method, a multi-tasking learning network is proposed by using the conditional Variable autoencoder (CVAE) model, which is one of the deep learning methods. In the experiments carried out, three different models were compared and the performance of the conditionally Variable autoencoder (CVAE) network trained with spline curves gave better results compared to other models.

关键词： Handwritting Generation conditional variational autoencoder Writing Style Spline Curve

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Improving Local Fidelity of LIME by CVAE 1st

Improving Local Fidelity of LIME by CVAE

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1st World Conference on Explainable Artificial Intelligence (XAI)

作者： Yasui, Daisuke Sato, Hirosh Kubo, Masao Natl Def Acad 1-10-20 Hashirimizu Yokosuka Kanagawa Japan

ISBN: (纸本)9783031440694;9783031440700

Knowing the basis of decisions is essential for using Machine Learning (ML) in various regions, such as medical diagnosis, automated driving, and organizational decision-making. The LIME algorithm is an XAI method that can be applied to many black-box models. However, there is a problem in that the local fidelity of the interpretable model decreases. The problem is due to the sampling and weighting step of LIME;using an autoencoder is an effective way to improve this. In this study, we aim to simultaneously improve the sampling and weighting of LIME in a classification task by using a conditional variational autoencoder and filtering samples by classes. Experiments were conducted to compare the local fidelity of the proposed and existing methods for neural network classifiers trained on three medical diagnostic data sets. The results show that the proposed method improves the local fidelity compared to the existing methods. In addition, we visualized the distribution of samples in the autoencoder latent space and conducted comparative experiments by attaching and detaching components of the existing and proposed methods to analyze the factors that improve fidelity.

关键词： LIME Local fidelity conditional variational autoencoder

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