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检索条件"主题词=variational autoencoder"

共 1535 条记录，以下是271-280 订阅

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ANOMALY DETECTION IN SQUARE BUTT JOINT BY FRICTION STIR WELDING USING variational autoencoder

ANOMALY DETECTION IN SQUARE BUTT JOINT BY FRICTION STIR WELD...

引用

International Symposium on Flexible Automation (ISFA)

作者： Oda, Kazuya Suwa, Haruhiko Murakami, Koji Setsunan Univ Grad Sch 17-8 Ikeda Naka Machi Neyagawa Osaka 5728508 Japan Setsunan Univ Dept Mech Engn 17-8 Ikeda Naka Machi Neyagawa Osaka 5728508 Japan YAMAMOTO MET TECHNOS 4-7Setoguchi 2 ChomeHirano Ku Osaka 5470034 Japan

ISBN: (纸本)9780791887882

This study proposes an anomaly detection method for the friction stir welding process using a variational autoencoder, which is a representative deep generative model in machine learning, with two time-series data: temperature near a tool probe tip and a tool shoulder tip and bending force on a tool. We mention a square butt welding process of a pair of aluminum alloy plates. Through preliminary welding experiments, normal and anomalous data are collected to construct a VAE network used for anomaly detection in square butt joining. The effectiveness of the proposed method is demonstrated through validation experiments by comparing the proposed VAE method with an autoencoder.

关键词： Anomaly detection Friction stir welding Machine learning variational autoencoder

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Self-supervised variational autoencoder for Recommender Systems 33

Self-supervised Variational Autoencoder for Recommender Syst...

引用

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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PRESERVATION OF ANOMALOUS SUBGROUPS ON variational autoencoder TRANSFORMED DATA

PRESERVATION OF ANOMALOUS SUBGROUPS ON VARIATIONAL AUTOENCOD...

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

作者： Maina, Samuel C. Bryant, Reginald E. Ogallo, William O. Varshney, Kush R. Speakman, Skyler Cintas, Celia Walcott-Bryant, Aisha Samoilescu, Robert-Florian Weldemariam, Komminist IBM Res Nairobi Kenya Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9781509066315

We investigate the effect of variational autoencoder (VAE) based data anonymization and its ability to preserve anomalous subgroup properties. We present a Utility Guaranteed Deep Privacy (UGDP) system which casts existing anomalous pattern detection methods as a new utility measure for data synthesis. UGDP's approach shows that properties of an anomalous subset of records, identified in the original data set, are preserved through the anonymization of a VAE. This is despite the newly generated records being completely synthetic. More specifically, the Bias-Scan algorithm identifies a subgroup of records that are consistently over- (or under-) risked by a black-box classifier as an area of 'poor fit'. This scanning process is applied on both pre- and post- VAE synthesized data. The areas of poor fit (i.e. anomalous records) persist in both settings. We evaluate our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach is able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset.

关键词： privacy bias anonymization variational autoencoder

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Squeezed Convolutional variational autoencoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things

Squeezed Convolutional Variational AutoEncoder for Unsupervi...

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International Conference on Information and Computer Technologies (ICICT)

作者： Kim, Dohyung Yang, Hyochang Chung, Minki Cho, Sungzoon Kim, Huijung Kim, Minhee Kim, Kyungwon Kim, Eunseok Seoul Natl Univ Dept Ind Engn Data Min Ctr Seoul South Korea Samsung Elect Co Ltd Data Analyt Lab Software R&D Ctr Seoul South Korea

ISBN: (纸本)9781538653845

In this paper, we propose Squeezed Convolutional variational autoencoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.

关键词： edge computing IIoT unsupervised anomaly Detection manufacturing variational autoencoder VAE convolutional neural network CNN squeezenet

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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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Complex Recurrent variational autoencoder for Speech Resynthesis and Enhancement

Complex Recurrent Variational Autoencoder for Speech Resynth...

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)9798350359329;9798350359312

Aiming at learning a probabilistic distribution over data, generative models have been actively studied with broad applications. This paper proposes a complex recurrent variational autoencoder (VAE) framework, for modeling time series data, particularly speech signals. First, to account for the temporal structure of speech signals, we introduce complex-valued recurrent neural network in the framework. Then, inspired by recent advancements in speech enhancement and separation, the reconstruction loss in the proposed model is L1-based loss, considering penalty on both complex and magnitude spectrograms. To exemplify the use of the complex generative model, we choose speech resynthesis first and then enhancement as the specific application in this paper. Experiments are conducted on the VCTK, TIMIT, and VoiceBank+DEMAND datasets. The results show that the proposed method can resynthesize complex spectrogram well, and offers improvements on objective metrics in speech intelligibility and signal quality for enhancement.

关键词： complex recurrent neural network variational autoencoder speech resynthesis speech enhancement

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Data Augmentation for Knowledge Tracing Based on variational autoencoder and Efficient Network Reusing 8th

Data Augmentation for Knowledge Tracing Based on Variational...

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8th International Joint Conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Zhao, Hui Sun, Jun Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819772438;9789819772445

Knowledge tracking (KT) is a task that predicting the degree of students' knowledge mastery through their learning interaction records. Although existing works improve predictive capability with well-designed neural network models or hypothetical learning mechanisms, the predictive performance is compromised in the scenarios of quantity limited interaction data. In this paper, we utilize variational autoencoder (VAE) and pre-trained network to generate question answer sequence data pairs related to the original interaction data, which can improve the performance of the model when added to the training set even in the case of data scarcity. Specifically, the steps of the data augmentation method for KT we proposed are as follows: 1) Question sequence generation. Generate latent question sequences that are similar to the real interaction question sequences from the pre-designed VAE model. 2) Answer sequence generation. Put the generated data into the pre-trained KT model to get reliable answer label sequences that correspond to latent question sequences. 3) Samples generation and training. Combine the two types of generated sequences as new samples for KT task training. We apply the data augmentation method on four classic datasets and demonstrate its effectiveness by reaching the state-of-the-art performance with an average AUC index improvement of 2.41%. We also verify the method on artificially random extracted data, and with only 20% of the data, it even achieves similar results compared with other methods using 100% of the data.

关键词： Knowledge tracking Sample generation Data augmentation variational autoencoder

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Enhancing Robot Self-Localization Accuracy and Robustness: A variational autoencoder-Based Approach 5

Enhancing Robot Self-Localization Accuracy and Robustness: A...

引用

5th International Conference on Control and Robotics (ICCR)

作者： Abe, Asuma Pongthanisorn, Goragod Kaneko, Shin-ichiro Capi, Genci Hosei Univ Dept Mech Engn Grad Sch Sci & Engn Tokyo Japan Natl Inst Technol Toyama Coll Dept Elect & Control Syst Engn Toyama Japan

ISBN: (纸本)9798350307627

Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the variational autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of VAE makes the system adaptive to varying environmental conditions. Our findings demonstrate that utilizing both the robot's coordinates and images as training data significantly enhances the accuracy of robot self-localization estimation and improves the robustness of the system due to sensor data noise.

关键词： robot localization variational autoencoder wheelchair robot indoor environments

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VAE-AD: Unsupervised variational autoencoder for Anomaly Detection in Hyperspectral Images 29th

VAE-AD: Unsupervised Variational Autoencoder for Anomaly Det...

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29th International Conference on Neural Information Processing

作者： Ojha, Nikhil Sinha, Indrajeet Kumar Singh, Krishna Pratap Indian Inst Informat Technol Allahabad Machine Learning & Optimizat Lab Dept Informat Technol Prayagraj UP India

ISBN: (纸本)9789819916474;9789819916481

Anomaly detection in hyperspectral images is an important and challenging problem. Most available data sets are unlabeled, and very few are labelled. In this paper, we proposed a lightweight variational autoencoder anomaly detector (VAE-AD) for hyperspectral data. VAE is used to learn the background distribution of the image, and thereafter it is used to construct a background representation for each pixel. Further reconstruction error is calculated between the background reconstructed image and the original image used for anomaly detection. A GMM-based post-processing step is used to construct the final detection map. The comparative analysis with five real-world hyperspectral data sets shows that the proposed model achieves better or comparable results with few learning parameters of the model, and with less time.

关键词： Anomaly detection variational autoencoder HSI hyperspectral imaging

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DualVAE: Dual Disentangled variational autoencoder for Recommendation

DualVAE: Dual Disentangled Variational AutoEncoder for Recom...

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SIAM International Conference on Data Mining (SDM)

作者： Guo, Zhiqiang Li, Guohui Li, Jianjun Wang, Chaoyang Shi, Si Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Software Engn Wuhan Peoples R China Wuhan Digital Engn Inst Wuhan Peoples R China Guangdong Lab Artificial Intelligence & Digital E Guangzhou Peoples R China

ISBN: (纸本)9781611978032

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled variational autoencoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.

关键词： Collaborative Recommendation Disentangled Representation Learning variational autoencoder

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