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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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INTEGRATION OF variational autoencoder AND SPATIAL CLUSTERING FOR ADAPTIVE MULTI-CHANNEL NEURAL SPEECH SEPARATION

INTEGRATION OF VARIATIONAL AUTOENCODER AND SPATIAL CLUSTERIN...

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IEEE Spoken Language Technology Workshop (SLT)

作者： Zmolikova, Katerina Delcroix, Marc Burget, Lukas Nakatani, Tomohiro Cernocky, Jan Honza Brno Univ Technol Fac IT IT4I Ctr Excellence Brno Czech Republic NTT Corp NTT Commun Sci Labs Kyoto Japan

ISBN: (纸本)9781728170664

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integrating spatial clustering with a spectral model was shown in several works. As the spectral model, previous works used either factorial generative models of the mixed speech or discriminative neural networks. In our work, we combine the strengths of both approaches, by building a factorial model based on a generative neural network, a variational autoencoder. By doing so, we can exploit the modeling power of neural networks, but at the same time, keep a structured model. Such a model can be advantageous when adapting to new noise conditions as only the noise part of the model needs to be modified. We show experimentally, that our model significantly outperforms previous factorial model based on Gaussian mixture model (DOLPHIN), performs comparably to integration of permutation invariant training with spatial clustering, and enables us to easily adapt to new noise conditions.

关键词： Multi-channel speech separation variational autoencoder spatial clustering DOLPHIN

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Self-supervised variational autoencoder for Recommender Systems 33

Self-supervised Variational Autoencoder for Recommender Syst...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Wang, Jing Liu, Gangdu Wu, Jun Jia, Caiyan Zhang, Zhifei Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

ISBN: (纸本)9781665408981

variational autoencoder (VAE) is considered as an emerging model for ensuring competitive performance in recommender systems. However, its performance is severely limited by the amount of training examples and, as a result, existing VAE models may fail to provide satisfactory recommendation results in presence of highly sparse user-item interactions. In this paper, we propose a self-supervised VAE model, SSVAE in short, to improve the generalization ability of VAE model on the sparse interaction datasets. Concretely, we first build multiple views for each user by data augmentation, and then design a pretext task to align the representations learned from different views of each user. Particularly, SSVAE aims to optimize a combined objective of recommendation task and pretext task, making them to reinforce each other during the learning process. Our encouraging experimental results on three real-world benchmarks validate the superiority of our SSVAE model to state-of-the-art VAE style recommendation techniques.

关键词： Recommender Systems Self-supervised Learning variational autoencoder

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Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using variational autoencoder

Human Initiated Grasp Space Exploration Algorithm for an Und...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Rolinat, Clement Grossard, Mathieu Aloui, Saifeddine Godin, Christelle Univ Paris Saclay CEA List F-91120 Palaiseau France Univ Grenoble Alpes CEA Leti F-38000 Grenoble France

ISBN: (纸本)9781728190778

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents an efficient procedure for exploring the grasp space of a multifingered adaptive gripper for generating reliable grasps given a known object pose. This procedure relies on a limited dataset of manually specified expert grasps, and use a mixed analytic and data-driven approach based on the use of a grasp quality metric and variational autoencoders. The performances of this method are assessed by generating grasps in simulation for three different objects. On this grasp planning task, this method reaches a grasp success rate of 99.91% on 7000 trials.

关键词： multifingered gripper grasp space exploration variational autoencoder grasp quality metric

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AMVAE: Asymmetric Multimodal variational autoencoder for Multi-view Representation 30th

AMVAE: Asymmetric Multimodal Variational Autoencoder for Mul...

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30th International Conference on Artificial Neural Networks (ICANN)

作者： Wen Youpeng Lin Hongxiang Guo Yiju Zhao Liang Dalian Univ Technol Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China

ISBN: (纸本)9783030863623;9783030863616

Real-world data are typically described using multiple modalities or multiple types of descriptors that are considered as multiple views. The data from different modalities locate in different subspaces, therefore the representations associated with similar semantics would be different. To solve this problem, many approaches have been proposed for fusion representation using data from multiple views. Although effectiveness achieved, most existing models lack precision for gradient diffusion. We proposed Asymmetric Multimodal variational autoencoder (AMVAE) to reduce the effect. The proposed model has two key components: multiple autoencoders and multimodal variational autoencoder. Multiple autoencoders are responsible for encoding view-specific data, while the multimodal variational autoencoder guides the generation of fusion representation. The proposed model effectively solves the problem of low precision. The experimental results show that our method is state of the art on several benchmark datasets for both clustering and classification tasks.

关键词： Multi-view representation variational autoencoder Deep learning

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TOWARD UNSUPERVISED 3D POINT CLOUD ANOMALY DETECTION USING variational autoencoder

TOWARD UNSUPERVISED 3D POINT CLOUD ANOMALY DETECTION USING V...

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IEEE International Conference on Image Processing (ICIP)

作者： Masuda, Mana Hachiuma, Ryo Fujii, Ryo Saito, Hideo Sekikawa, Yusuke Keio Univ Tokyo Japan Denso IT Lab Tokyo Japan

ISBN: (纸本)9781665441155

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method.

关键词： 3D point cloud anomaly detection unsupervised learning variational autoencoder

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Federated variational autoencoder for Collaborative Filtering

Federated Variational Autoencoder for Collaborative Filterin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Polato, Mirko Univ Padua Dept Math Padua Italy

ISBN: (纸本)9780738133669

Recommender Systems (RSs) are valuable technologies that help users in their decision-making process. Generally, RSs are designed with the assumption that a central server stores and manages historical users' behaviors. However, users are nowadays more aware of privacy issues leading to a higher demand for privacy-preserving technologies. To cope with this issue, the Federated Learning (FL) paradigm can provide good performance without harming the users' privacy. Some efforts have been devoted to adapt standard collaborative filtering methods (e.g., matrix factorization) into the FL framework in recent years. In this paper, we present a Federated variational autoencoder for Collaborative Filtering (FedVAE), which extends the state-of-the-art MultVAE model. Additionally, we propose an adaptive learning rate schedule to accelerate learning. We also discuss the potential privacy-preserving capabilities of FedVAE. An extensive experimental evaluation on five benchmark data sets shows that our proposal can achieve performance close to MultVAE in a reasonable number of iterations. We also empirically demonstrate that the adaptive learning rate guarantees both accelerated learning and good stability.

关键词： federated learning variational autoencoder collaborative filtering recommender systems top-n item recommendation

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Data augmentation using a variational autoencoder for estimating property prices

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PROPERTY MANAGEMENT 2021年第3期39卷 408-418页

作者： Lee, Changro Kangwon Natl Univ Chunchon South Korea

Purpose Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques. Design/methodology/approach The deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance. Findings The results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry. Originality/value Although deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

关键词： Deep learning Data augmentation variational autoencoder Property valuation

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Fault Detection and Diagnosis in Industrial Processes with variational autoencoder: A Comprehensive Study

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SENSORS 2022年第1期22卷 227页

作者： Zhu, Jinlin Jiang, Muyun Liu, Zhong Jiangnan Univ State Key Lab Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

关键词： process monitoring deep model variational autoencoder deep reconstruction dynamic process

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Automatic Mask Detecion using Convolutional Neural Networks and variational autoencoder 15

Automatic Mask Detecion using Convolutional Neural Networks ...

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IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Silabela, Mxolisi Bogdandy, Bence Toth, Zsolt Eszterhazy Karoly Univ Fac Informat Eger Hungary

ISBN: (纸本)9781728195438

The importance of proper hygienical behaivour is essential in today's word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.

关键词： Machine Learning Feedforward neural networks variational autoencoder Image Recognition Deep learning

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