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检索条件"主题词=variational autoencoder"

共 1532 条记录，以下是351-360 订阅

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Conditioned variational autoencoder for Top-N Item Recommendation 31st

Conditioned Variational Autoencoder for Top-N Item Recommend...

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Carraro, Tommaso Polato, Mirko Bergamin, Luca Aiolli, Fabio Univ Padua Dept Math Padua Italy Fondazione Bruno Kessler FBK Trento Italy Univ Turin Dept Comp Sci Turin Italy

ISBN: (纸本)9783031159312;9783031159305

State-of-the-art recommender systems (RSs) generally try to improve the overall recommendation quality. However, users usually tend to explicitly filter the item set based on available categories, e.g., smartphone brands, movie genres. For this reason, an RS that can make this step automatically is likely to increase the user's experience. This paper proposes a Conditioned variational autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to a standard VAE in which a condition vector is fed into the encoder. The constrained ranking is learned during training thanks to a new reconstruction loss that takes the input condition into account. We show that our model generalizes the state-of-the-art Mult-VAE collaborative filtering model. Experimental results underline the potential of CVAE in providing accurate recommendations under constraints. Finally, the performed analyses suggest that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation.

关键词： Recommender systems Collaborative filtering Implicit feedback variational autoencoder Top-N recommendation

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Detecting Anomalies in Longitudinal Elevation of Track Geometry Using Train Dynamic Responses via a variational autoencoder

Detecting Anomalies in Longitudinal Elevation of Track Geome...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Liu, Jingxiao Wei, Yujie Berges, Mario Bielak, Jacobo Garrett, James H. Noh, Hae Young Carnegie Mellon Univ Dept Civil & Environm Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

ISBN: (纸本)9781510625969

Track geometry is one of the most important health indices in the maintenance of rail tracks. Visual inspection and inspection using a track-geometry car are two common approaches to inspect track geometry. Presently, using accelerations from in-service trains has become a popular track inspection approach, because it is a low-cost way to monitor the rail tracks more frequently. However, due to the noise presented in the collected accelerations, detecting anomalies using manually designed features often results in many false alarms. In this paper, we propose a learning-based anomaly detection approach for monitoring the longitude elevation of track geometry from the dynamic response of an in-service train. We consider the track geometry with a sudden change as an anomaly, measured by the signal energy of slopes of the track geometry. The proposed approach uses a variational autoencoder (VAE) to detect the anomaly. The VAE takes accelerations as input and learns a mapping from the frequency-domain representation of acceleration signals to a low-dimensional latent space that represents the distribution of the observed data. The reconstruction probability, which measures the variability of the distribution of the input data, is used as an anomaly score for indicating how well the input follows the normal pattern. Compared to distance- and density-based anomaly detection methods, such as K-nearest neighbor and clustering, the VAE-based anomaly detection is robust to measurement noise and prevents overfitting because it captures the underlying distribution of the data in a low-dimensional space. Furthermore, the VAE-based method does not require model-specific thresholds for detecting anomalies because it uses a probabilistic measurement instead of reconstruction error as the anomaly score. We validate the proposed VAE-based approach on the vibration dataset from an in-service train. We show that this approach outperforms a baseline model (an autoencoder-based anomaly de

关键词： Anomaly detection track geometry inspection indirect structural health monitoring variational autoencoder

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MusiciAI: A Hybrid Generative Model for Music Therapy using Cross-Modal Transformer and variational autoencoder 2

MusiciAI: A Hybrid Generative Model for Music Therapy using ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Venkatachalam, Nirmala Raghuram Karthic Ganesh, V. Hari Vigneshwaran, S. Easwari Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Music has long been recognized for its therapeutic potential, offering emotional and psychological benefits. In the contemporary landscape, the intersection of artificial intelligence and music therapy introduces innovative applications. MusiciAI introduces an advanced generative model designed to harness the healing power of music through a novel hybrid approach. Combining a Cross-Modal Transformer and a variational autoencoder, MusiciAI interprets textual prompts and generates diverse, emotionally resonant music. The proposed model is trained on a carefully curated dataset, incorporating language models for precise annotation. MusiciAI integrates user interaction, allowing prompt-based music generation and user adjustments for a personalized therapeutic experience. Through a comprehensive exploration of the hybrid model's architecture, training, evaluation, and user-centric features, and contributes to the evolving landscape of generative AI in music therapy. © 2024 IEEE.

关键词： Artificial Intelligence Cross-Modal Transformer Emotional Resonance Generative AI Hybrid Modal Language Models Music Therapy Personalized Music User Interaction variational autoencoder

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An Application of Geometric Aspects of variational autoencoder Model to Forgery Detection of Scanned Documents 13

An Application of Geometric Aspects of Variational Autoencod...

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13th International Conference on Machine Vision

作者： Janiszewski, Igor Slugin, Dmitry Andreeva, Elena Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia Smart Engines Serv LLC Moscow Russia

ISBN: (纸本)9781510640412

The paper proposes an approach for matching of digitized copies of business documents. This task arises when comparing two versions of the same document - genuine and forgery - to find possible modifications, for example in the banking sector during the conclusion of contracts in paper form to avoid possible fraud. The matching method of two documents based on comparison images of text lines using variational autoencoder (VAE) trained on genuine images and calculation Fisher information metric to find modifications. Experiments were conducted on the public Payslips dataset (in French). The results show the high quality and reliability of finding document forgeries and are compared to the results of the method which applies OCR and image matching.

关键词： Forgery detection documents matching variational autoencoder Fisher information metric

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The Character Generation in Handwriting Feature Extraction using variational autoencoder 14

The Character Generation in Handwriting Feature Extraction u...

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14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Yamada, Tomoki Hosoe, Mariko Kato, Kunihito Yamamoto, Kazuhiko Gifu Univ Fac Engn Gifu Japan Forens SciLab Gifu PrefPolice HQ Gifu Prefectural Police Headquarters Gifu Japan

ISBN: (纸本)9781538635865

Handwriting identification is a method to identify an unknown writer by comparing a known writer's characters with an unknown writer's characters by using the homeostasis of handwriting in the characters. In handwriting identification, improving the accuracy is important in analyzing a large number of characters of the same class. However, a problem with the accuracy of handwriting identification occurs because of lack of characters of the same class. In order to solve this problem, we propose a method of extracting features of handwritten characters from a character class and generating characters of different character class based on the extracted features. It is difficult to extract handwriting features by scientific numerical analysis method, and to generate characters from extracted handwriting features. In this paper, we tried to construct a learning method using Deep Learning to generate a model of the handwriting feature extraction and the handwriting character data generative model. Using the existing neural network model, we tried to apply the generative model of the data of Japanese characters (Hiragana). Because existing models cannot capture complicated character features like Hiragana, we propose a modified method to improve character generation accuracy by adding a convolution model to the existing neural network model. Improvement of character generation accuracy by adding the convolution model was confirmed by using quantitative evaluation method of generated characters.

关键词： variational autoencoder Generative model Character generation

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Personalized Arrhythmia Detection Based on Lightweight autoencoder and variational autoencoder 33rd

Personalized Arrhythmia Detection Based on Lightweight Autoe...

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33rd Australasian Database Conference (ADC)

作者： Zhong, Zhaoyi Sun, Le Subramani, Sudha Nanjing Univ Informat Sci & Technol Minist Educ Engn Res Ctr Digital Forens Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Dept Jiangsu Collaborat Innovat Ctr Atmospher Env Nanjing 210044 Peoples R China Victoria Univ Footscray Vic Australia

ISBN: (纸本)9783031155123;9783031155116

Arrhythmia has become one of the important causes of human death. The research on arrhythmia detection has great medical value. In reality, patients' arrhythmia heartbeat is much less than the normal heartbeat. Supervised classifiers often have the problem of imbalanced training data. Therefore, we propose an unsupervised personalized arrhythmia detection system, called PerAD. PerAD trains a lightweight autoencoder ShaAE for each user for arrhythmia detection. ShaAE only needs to use the user's personal normal data for training. The encoder and decoder of ShaAE are composed of a lightweight network ShaRNN. ShaRNN is a two-layer RNN structure that can process data in parallel. Thus, ShaAE is easy to deploy to edge wearable devices. We also design a fast-inference variational autoencoder to generate normal simulation samples to assist in training ShaAE. We test ShaAE on MIT-BIH Arrhythmia Database. ShaAE without using simulation data to assist training can achieve 96.86% accuracy. ShaAE using simulation samples to assist training can achieve accuracy of 97.11% and has 6.19% higher performance than state-of-the-art for f1 score.

关键词： Deep learning Arrhythmia detection Unsupervised learning Lightweight autoencoder variational autoencoder

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UNSUPERVISED MULTIPLE DOMAIN TRANSLATION THROUGH CONTROLLED DISENTANGLEMENT IN variational autoencoder 49

UNSUPERVISED MULTIPLE DOMAIN TRANSLATION THROUGH CONTROLLED ...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Almudevar, Antonio Mariotte, Theo Ortega, Alfonso Tahon, Marie Univ Zaragoza Aragon Inst Engn Res I3A ViVoLab Zaragoza Spain Le Mans Univ LIUM Le Mans France

ISBN: (纸本)9798350344868;9798350344851

Unsupervised Multiple Domain Translation is the task of transforming data from one domain to other domains without having paired data to train the systems. Typically, methods based on Generative Adversarial Networks (GANs) are used to address this task. However, our proposal exclusively relies on a modified version of a variational autoencoder. This modification consists of the use of two latent variables disentangled in a controlled way by design. One of this latent variables is imposed to depend exclusively on the domain, while the other one must depend on the rest of the variability factors of the data. Additionally, the conditions imposed over the domain latent variable allow for better control and understanding of the latent space. We empirically demonstrate that our approach works on different vision datasets improving the performance of other well known methods. Finally, we prove that, indeed, one of the latent variables stores all the information related to the domain and the other one hardly contains any domain information.

关键词： multiple domain translation controlled disentanglement variational autoencoder

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AUTOREGRESSIVE variational autoencoder WITH A HIDDEN SEMI-MARKOV MODEL-BASED STRUCTURED ATTENTION FOR SPEECH SYNTHESIS 47

AUTOREGRESSIVE VARIATIONAL AUTOENCODER WITH A HIDDEN SEMI-MA...

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

作者： Fujimoto, Takato Hashimoto, Kei Nankaku, Yoshihiko Tokuda, Keiichi Nagoya Inst Technol Nagoya Aichi Japan

ISBN: (纸本)9781665405409

This paper proposes an autoregressive speech synthesis model based on the variational autoencoder incorporating latent sequence representation for acoustic and linguistic features and the structure of a hidden semi-Markov model (HSMM). Although autoregressive models can provide efficient and accurate modeling of acoustic features, they have exposure bias, i.e., the mismatch between training (teacher-forcing) and inference (free-running). To overcome this problem, we introduce an autoregressive latent variable sequence, rather than using autoregressive generation of observations. Latent representation of alignment using HSMM-based structured attention mechanism enables the use of a completely consistent training algorithm for acoustic modeling with explicit duration models. Experimental results indicate that the proposed model outperformed baselines in subjective naturalness.

关键词： speech synthesis variational autoencoder autoregressive model attention mechanism hidden semi-Markov model

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Unsupervised Domain Adaptation via variational autoencoder with Explicit and Implicit Feature Alignment for Cardiac Segmentation 8

Unsupervised Domain Adaptation via Variational Autoencoder w...

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8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Li, Yan Wang, Yifan Zheng, Fan Cheng, Linkai Xu, Di Cui, Hengfei Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Huawei Cloud Media Innovat Lab Xian Peoples R China Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen Peoples R China

ISBN: (纸本)9781665455336

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of vital importance in the diagnosis of myocardial infarction (MI). However, the LGE image annotations are limited, it is urgently needed to transfer the learned knowledge in other modalities. We propose an unsupervised domain adaptation (UDA) algorithm based on variational autoencoder (VAE) with a Combination of Explicit and Implicit Alignment of feature distributions (VCEIA). First, we re-derive the Kullback-Leibler (KL) divergence of the features extracted in the source and target domains as a measure of distribution discrepancy. Secondly, for explicit category-level distribution alignment, we use contrast learning to reduce the distance between prototypes of the same category in the source and target domains. Finally, we add a domain discriminator to the output space to implicitly align the feature distributions. Extensive experiments were conducted to validate the effectiveness of VCEIA on the MS-CMRSeg 2019 dataset, which showed competitive results.

关键词： cardiac segmentation domain adaptation distribution alignment variational autoencoder

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Designing Novel Functional Peptides by Manipulating a Temperature in the Softmax Function Coupled with variational autoencoder

Designing Novel Functional Peptides by Manipulating a Temper...

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IEEE International Conference on Big Data (Big Data)

作者： Chen, Shuan Kim, Hyun Uk Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn KAIST Inst Artificial Intelligence Daejeon South Korea

ISBN: (纸本)9781728108582

Development of an efficient peptide design method is crucial for tackling medical problems, such as designing antimicrobial peptides for combating drug resistant pathogens and anticancer peptides for various cancers. Here, we present variational autoencoder (VAE) coupled with a Softmax function having a temperature factor (1) for high-throughput design of novel functional peptides. VAE is a generative machine learning model, which has proved to be useful for generating peptide sequences. In this study, we additionally use a Softmax function with T to facilitate determining the most probable amino acids at each position of peptide sequences to be generated, which is difficult to achieve using a conventional VAE. In particular, by manipulating T in the Softmax function, we select biologically most feasible peptides with a desired function. This method is demonstrated for designing novel antimicrobial and anticancer peptides in this study. The method presented herein should be useful for designing various peptides with a desired function upon availability of relevant datasets.

关键词： peptide design Softmax function with a temperature factor variational autoencoder

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