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16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Liang, Kaiyun He, Fazhi Zhu, Yuanyuan Gao, Xiaoxin Wuhan Univ Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9789811945465;9789811945458

Robot localization, the task of determining the current pose of a robot, is a crucial problem of mobile robotic. Visual-based robot localization, which using only cameras as exteroceptive sensors, has become extremely popular due to the relatively cheap cost of cameras. However, current approaches such as Bayes Filter based methods and Visual Odometry need knowledge of prior location and also rely on the feature points in images. This paper presents a novel semi-supervised learning method based on variational autoencoder (VAE) for visual-based robot localization, which does not rely on the prior location and feature points. Because our method does not need prior knowledge, it also can be used as a correction of dead reckoning. We adopt VAE as an unsupervised learning method to preprocess the environment images, followed by a supervised learning model to learn the mapping between the robot's location and processed images. Therefore, one merit of the proposed approach is that it can adopt any state-of-the-art supervised learning models. Furthermore, this semi-supervised learning scheme is also suitable for improving other supervised learning problems by adding extra unlabeled data to the training data set to solve the problem in a semi-supervised manner. We show that this semi-supervised learning scheme can get a high accuracy for pose prediction using a surprisingly small number of labeled images compared to other machine learning methods.

关键词： Robot localization Semi-supervised learning variational autoencoder Neural network

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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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Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic variational autoencoder 6

Out of Distribution Reasoning by Weakly-Supervised Disentang...

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6th International Conference on System Reliability and Safety (ICSRS)

作者： Rahiminasab, Zahra Yuhas, Michael Easwaran, Arvind Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Energy Res Inst Singapore Singapore

ISBN: (纸本)9781665470926

Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine learning models. Recently there have been promising results for OOD detection in the latent space of variational autoencoders (VAEs). However, without disentanglement, VAEs cannot perform OOD reasoning. Disentanglement ensures a one-to-many mapping between generative factors of OOD (e.g., rain in image data) and the latent variables to which they are encoded. Although previous literature has focused on weakly-supervised disentanglement on simple datasets with known and independent generative factors. In practice, achieving full disentanglement through weak supervision is impossible for complex datasets, such as Carla, with unknown and abstract generative factors. As a result, we propose an OOD reasoning framework that learns a partially disentangled VAE to reason about complex datasets. Our framework consists of three steps: partitioning data based on observed generative factors, training a VAE as a logic tensor network that satisfies disentanglement rules, and run-time OOD reasoning. We evaluate our approach on the Carla dataset and compare the results against three state-of-the-art methods. We found that our framework outperformed these methods in terms of disentanglement and end-to-end OOD reasoning.

关键词： Out-of-distribution reasoning Weakly-supervised disentanglement variational autoencoder Logic tensor network

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Nonlinear quality-related fault detection using combined deep variational information bottleneck and variational autoencoder

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ISA TRANSACTIONS 2021年 114卷 444-454页

作者： Tang, Peng Peng, Kaixiang Dong, Jie Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Deep learning has gotten much attention in industrial field, many fault detection methods based on deep learning have been developed for nonlinear industrial processes. However, most of them do not take the quality-related faults into account. In order to extract the latent variables which can represent the separated quality-related and unrelated information, this paper proposes a novel deep VIB-VAE algorithm, which combines variational autoencoder (VAE) model and deep variational information bottleneck (VIB). Deep VIB extracts quality-related latent variables by maximizing mutual information between latent variables and process quality while minimizing mutual information between latent variables and observation. VAE is used to learn the quality-unrelated part with above quality-related latent variables as auxiliary information. To monitor and distinguish quality-related and quality-unrelated faults, two monitoring statistics are designed by the two-part latent variables. The reconstruction error by VAE is introduced to improve the performance of fault detection. Finally, the effectiveness of the proposed deep VIB-VAE algorithm is demonstrated by a numerical case and a real hot strip mill process case, respectively. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fault detection Quality-related Deep variational information bottleneck variational autoencoder Hot strip mill process

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Advances Toward the Next Generation Fire Detection: Deep LSTM variational autoencoder for Improved Sensitivity and Reliability

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IEEE ACCESS 2021年 9卷 30636-30653页

作者： Xu, Zhaoyi Guo, Yanjie Saleh, Joseph Homer Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Fire detection is a critical component of a building safety monitoring system and remains an important research area with weighty practical relevance. Significant advances have occurred in recent years in building automation, and the operation of buildings has become more complex and requires ever more effective monitoring systems. In this work, we develop a novel fire detection method using deep Long-Short Term Memory (LSTM) neural networks and variational autoencoder (VAE) to meet these increasingly stringent requirements and outperform existing fire detection methods. To evaluate the effectiveness of our method, we develop high-fidelity simulations, and we use datasets from real-world fire and non-fire experiments provided by NIST. We compare and discuss the performance of our proposed fire detection with alternative methods, including the standard LSTM, cumulative sum control chart (CUSUM), exponentially weighted moving average (EWMA), and two currently used fixed-temperature heat detectors. The results using the simulation-based and the real-world experiments are complementary, and they indicate that the LSTM-VAE robustly outperforms the other detection methods with, for example, statistically significant shorter alarm time lags, no missed detection, and no false alarms. The results also identify shortcomings of other detection methods and indicate a clear ranking among them (LSTM-VAE > EWMA > LSTM > CUSUM).

关键词： Fire detection machine learning anomaly detection long short-term memory variational autoencoder

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Unsupervised Deep Learning based variational autoencoder Model for COVID-19 Diagnosis and Classification

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PATTERN RECOGNITION LETTERS 2021年 151卷 267-274页

作者： Mansour, Romany F. Escorcia-Gutierrez, Jose Gamarra, Margarita Gupta, Deepak Castillo, Oscar Kumar, Sachin New Valley Univ Dept Math Fac Sci El Kharga 72511 Egypt Univ Autonoma Caribe Elect & Telecommun Program Barranquilla Colombia Univ Costa Dept Computat Sci & Elect CUC Barranquilla Colombia Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India Tijuana Inst Technol Tijuana Mexico South Ural State Univ Dept Comp Sci Chelyabinsk Russia

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively. (c) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 Deep learning Unsupervised learning variational autoencoder Image classification

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Case2vec: joint variational autoencoder for case text embedding representation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第9期12卷 2517-2528页

作者： Song, Ran Gao, Shengxiang Yu, Zhengtao Zhang, Yafei Zhou, Gaofeng Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China

The embedding representation of the case text represent text as vector which consist information of original texts abundantly. Text embedding representation usually uses text statistical features or content features alone. However, case texts have characteristics that include similar structure, repeated words, and different text lengths. And the statistical feature or content feature cannot represent case text efficiently. In this paper, we propose a joint variational autoencoder (VAE) to represent case text embedding representation. We consider the statistical features and content features of case texts together, and use VAE to align the two features into the same space. We compare our representations with existing methods in terms of quality, relationship, and efficiency. The experiment results show that our method has achieved good results, which have higher performance than the model using single feature.

关键词： Embedding representation Statistical feature Content feature variational autoencoder

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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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A memory pool variational autoencoder framework for cross-domain recommendation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Yang, Jie Zhu, Jianxiang Ding, Xiaofeng Peng, Yaxin Zhang, Yangchun Shanghai Univ Coll Sci Dept Math Shanghai 200444 Peoples R China HiSilicon Technol Co Ltd Shanghai 201206 Peoples R China

Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On account of fully utilizing information, various relationships among users and items are taken into account, i.e., the interaction relationship between users and their corresponding items;the relationship among users or items;and the indirect relationship between the user and items related to other users. In order to process these relationships, we propose a novel framework named Memory Pool variational autoencoder (MPVAE). The main advantages of the MPVAE model lie in three aspects: (1) it generates the embedding representations that incorporate more information by a memory pool mechanism in the source and target domains;(2) it involves the relationship among users or items efficiently by the similarity measurement, further, the indirect relationship can be explicitly described, which makes full use of information in the source domain;and (3) it leverages the superiority of the probability model from the perspective of the VAE structure, which ensures generation and robustness. Comprehensive experiments on three real datasets show that the proposed model achieves remarkable superiority over several competitive CDR models.

关键词： Attention mechanism Cross-domain recommendation Cold-start 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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