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检索条件"主题词=Variational autoencoder"

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VAE-based anomaly detection for embedded computer electronic components 22

VAE-based anomaly detection for embedded computer electronic...

引用

Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

作者： Shuda Gao Zhong Ma Zhanzhuang He Yuanhong Mao Xi'an Microelectronic Technology Institute China

ISBN: (纸本)9781450397773

The current maintenance of aerospace equipment generally uses regular maintenance, scheduled maintenance, seasonal maintenance, after-the-fact maintenance, and replacement maintenance. These methods are ill-timed, time-consuming, and wasteful of materials. Monitoring the reliability and healthy operating status of each embedded computer electronic component is essential, and maintenance staff will benefit greatly from a data-driven approach to anomaly detection. It can be altered from "repair afterward" to "repair as necessary" and from " repair regularly" to "repair at any time" to solve the practical problems arising from maintenance. The variational autoencoder (VAE), which is based on the component storage aging acceleration data, is used in this paper to model the component's normal operating status and perform anomaly detection. The precision and recall of this anomaly detection method are 0.950 and 0.977. This method evaluates the operating status and reliability of each component, improves the reliability and service life of the computer, and establishes the technological framework for the next generation of computer Prognostics and Health Management (PHM) systems.

关键词： Anomaly detection variational autoencoder Embedded computer

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EXTENDING MUSIC BASED ON EMOTION AND TONALITY VIA GENERATIVE ADVERSARIAL NETWORK

EXTENDING MUSIC BASED ON EMOTION AND TONALITY VIA GENERATIVE...

引用

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

作者： Tseng, Bo-Wei Shen, Yih-Liang Chi, Tai-Shih Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781728176055

We propose a generative model for music extension in this paper. The model is composed of two classifiers, one for music emotion and one for music tonality, and a generative adversarial network (GAN). Therefore, it can generate symbolic music not only based on low level spectral and temporal characteristics, but also on high level emotion and tonality attributes of previously observed music pieces. The generative model works in a universal latent space constructed by the variational autoencoder (VAE) for representing music pieces. We conduct subjective listening tests and derive objective measures for performance evaluation. Experimental results show that the proposed model produces much smoother and more authentic music pieces than the baseline model in terms of all subjective and objective measures.

关键词： Music generation variational autoencoder generative adversarial network music emotion tonality

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Improving Gaussian mixture latent variable model convergence with Optimal Transport 13

Improving Gaussian mixture latent variable model convergence...

引用

13th Asian Conference on Machine Learning (ACML)

作者： Gaujac, Benoit Feige, Ilya Barber, David UCL London England

Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in training often manifesting in the discrete latent variable not being leveraged. In this paper, we show why such models struggle to train using traditional log-likelihood maximization, and that they are amenable to training using the Optimal Transport framework of Wasserstein autoencoders. We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning. Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden. Furthermore, the discrete latent provides significant control over generation.

关键词： Optimal Transport Wasserstein autoencoder variational autoencoder Latent variable modeling Generative modeling

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PITCH-TIMBRE DISENTANGLEMENT OF MUSICAL INSTRUMENT SOUNDS BASED ON VAE-BASED METRIC LEARNING

PITCH-TIMBRE DISENTANGLEMENT OF MUSICAL INSTRUMENT SOUNDS BA...

引用

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

作者： Tanaka, Keitaro Nishikimi, Ryo Bando, Yoshiaki Yoshii, Kazuyoshi Morishima, Shigeo Waseda Univ Tokyo Japan Kyoto Univ Kyoto Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan Waseda Res Inst Sci & Engn Tokyo Japan

ISBN: (纸本)9781728176055

This paper describes a representation learning method for disentangling an arbitrary musical instrument sound into latent pitch and timbre representations. Although such pitch-timbre disentanglement has been achieved with a variational autoencoder (VAE), especially for a predefined set of musical instruments, the latent pitch and timbre representations are outspread, making them hard to interpret. To mitigate this problem, we introduce a metric learning technique into a VAE with latent pitch and timbre spaces so that similar (different) pitches or timbres are mapped close to (far from) each other. Specifically, our VAE is trained with additional contrastive losses so that the latent distances between two arbitrary sounds of the same pitch or timbre are minimized, and those of different pitches or timbres are maximized. This training is performed under weak supervision that uses only whether the pitches and timbres of two sounds are the same or not, instead of their actual values. This improves the generalization capability for unseen musical instruments. Experimental results show that the proposed method can find better-structured disentangled representations with pitch and timbre clusters even for unseen musical instruments.

关键词： Disentangled representation learning variational autoencoder metric learning pitch and timbre modeling

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Enforcing Individual Fairness via Renyi variational Inference 28th

Enforcing Individual Fairness via Renyi Variational Inferenc...

引用

28th International Conference on Neural Information Processing

作者： Grari, Vincent El Hajouji, Oualid Lamprier, Sylvain Detyniecki, Marcin Sorbonne Univ LIP6 CNRS Paris Paris France Ecole Polytech Palaiseau France AXA REV Res Paris Paris France

ISBN: (纸本)9783030923068;9783030923075

As opposed to group fairness algorithms which enforce equality of distributions, individual fairness aims at treating similar people similarly. In this paper, we focus on individual fairness regarding sensitive attributes that should be removed from people comparisons. In that aim, we present a new method that leverages the variational autoencoder (VAE) algorithm and the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation coefficient for enforcing individual fairness in predictions. We also propose new metrics to assess individual fairness. We demonstrate the effectiveness of our approach in enforcing individual fairness on several machine learning tasks prone to algorithmic bias.

关键词： Individual fairness Neural network variational autoencoder

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Interference Motion Removal for Doppler Radar Vital Sign Detection Using variational Encoder-Decoder Neural Network

Interference Motion Removal for Doppler Radar Vital Sign Det...

引用

IEEE Radar Conference (RadarConf)

作者： Czerkawski, Mikolaj Ilioudis, Christos Clemente, Carmine Michie, Craig Andonovic, Ivan Tachtatzis, Christos Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

ISBN: (纸本)9781728176093

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. It is demonstrated that the application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate.

关键词： Doppler radar heart rate monitoring respiration rate monitoring vital signs random body movement variational autoencoder

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Exploiting Generative Models for Performance Predictions of 3D Car Designs

Exploiting Generative Models for Performance Predictions of ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Saha, Sneha Rios, Thiago Minku, Leandro L. Stein, Bas Vas Wollstadt, Patricia Yao, Xin Back, Thomas Sendhoff, Bernhard Menzel, Stefan Honda Res Inst Europe GmbH Carl Legien Str 30 D-63073 Offenbach Germany Univ Birmingham Sch Comp Sci CERCIA Birmingham W Midlands England Leiden Inst Adv Comp Sci LIACS Niels Bohrweg 1 NL-2333 CA Leiden Netherlands Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728190488

In automotive digital development, engineers utilize multiple virtual prototyping tools to design and assess the performance of 3D shapes. However, accurate performance simulations are computationally expensive and time-consuming, which may be prohibitive for design optimization tasks. To address this challenge, we envision a 3D design assistance system for design exploration with performance assessment in the automotive domain. Recent advances in deep learning methods for learning geometric data are a promising step towards realizing such systems. Deep learning-based (variational) autoencoder models have been used for learning and compressing 3D data allowing engineers to generate low-dimensional representations of 3D designs. Finding representations in a data-driven fashion results in representations that are agnostic to downstream tasks performed on these representations and are believed to capture relevant design features. In this paper, we evaluate whether such data-driven representations contain relevant information about the input data and whether representations are meaningful in performance prediction tasks for the input data. We use machine learning-based surrogate models to predict the performances of car shapes based on the low-dimensional representation learned by 3D point cloud (variational) autoencoders. Furthermore, we exploit the stochastic nature of the representation learned by variational autoencoders to augment the training data for our surrogate models, since the limited amount of data is usually a challenge for surrogate modeling in engineering. We demonstrate that augmenting training with generated shapes improves prediction accuracy. In sum, we find that geometric deep learning approaches offer powerful tools to support the engineering design process.

关键词： representation learning 3D point clouds autoencoder variational autoencoder regression analysis

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Dungeon and Platformer Level Blending and Generation using Conditional VAEs

Dungeon and Platformer Level Blending and Generation using C...

引用

IEEE Conference on Games (IEEE CoG)

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781665438865

variational autoencoders (VAEs) have been used in prior works for generating and blending levels from different games. To add controllability to these models, conditional VAEs (CVAEs) were recently shown capable of generating output that can be modified using labels specifying desired content, albeit working with segments of levels and platformers exclusively. We expand these works by using CVAEs for generating whole platformer and dungeon levels, and blending levels across these genres. We show that CVAEs can reliably control door placement in dungeons and progression direction in platformer levels. Thus, by using appropriate labels, our approach can generate whole dungeons and platformer levels of interconnected rooms and segments respectively as well as levels that blend dungeons and platformers. We demonstrate our approach using The Legend of Zelda, Metroid, Mega Man and Lode Runner.

关键词： PCGML variational autoencoder level generation game blending

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Generate Xi'an Drum Music Based on Compressed Coding 40

Generate Xi'an Drum Music Based on Compressed Coding

引用

40th Chinese Control Conference (CCC)

作者： Wang, Haochen Li, Jiabao Lin, Yuxuan Ru, Wen Wu, Jiang Vrije Univ Amsterdam Comp Sci Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands Xi An Jiao Tong Univ Sch Qianxuesen Xian 710043 Peoples R China Xi An Jiao Tong Univ Sch Artificial Intelligence Xian 710043 Peoples R China Xi An Jiao Tong Univ Sch Human & Social Sci Xian 710043 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710043 Peoples R China

ISBN: (纸本)9789881563804

Xi'an Drum Music is a traditional form of Chinese music and its notes are recorded by Chinese Characters. Because Xi'an Drum Music is composed and translated by the elder musicians, Xi'an Drum Music becomes difficult to be protected. In this article, we use sparse coding and compressed coding to transfer the Chinese Character recording to genre and lyrics of Xi'an Drum Music. Based on our dataset of Xi'an Drum Music, we set up a method to generate Xi'an Drum Music similar to Huffman coding, a model named Xi'an Drum Music Generation via variational autoencoder (DMGVAE) and the accuracy of Xi'an Drum Music generation increases to 0.73. Compressed coding on Xi'an Drum Music shows a novel method to generate Xi'an Drum Music by compressed format, making potential application for generating the sparse traditional Chinese Music such as Xi'an Drum Music.

关键词： Xi'an Drum Music Compressed Coding variational autoencoder Deep Learning

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Generating Explanations for Recommendation Systems via Injective VAE 21

Generating Explanations for Recommendation Systems via Injec...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Cai, Zerui East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665423984

Generating explanations for recommendation systems is essential for improving its transparency since informative explanations such as generated reviews can help users comprehend the reason for receiving a specified recommendation. The generated reviews should be specific for the given user, item, and rating, however, recent works only focus on designing more and more powerful decoder, merely treating this task as a plain natural language generation process. We argue that there may exist the risk that the powerful decoder neglects the input embeddings and suffers from the biases that exist in data. In this paper, we propose a novel Injective variational autoencoders (InVAE) for generating high-quality reviews. Specifically, we employ a Collaborative Kullback-Leibler divergences (CKL) mechanism to building a better latent space that captures meaningful information. Base on this, the Spectral Regularization on Flow-based transformation (SRF) method is designed to backward transfer the priorities of generated latent variables to the input embeddings. Therefore, our method can construct more informative input embeddings and provides more specific explanations for different inputs. Extensive empirical experiments demonstrate that our model can construct much more meaningful feature embeddings and generate personalized reviews in high quality.

关键词： Recommendation System Explanation Generation variational autoencoder

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