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检索条件"主题词=variational autoencoder"

共 1537 条记录，以下是671-680 订阅

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Interpreting variational autoencoders with Fuzzy Logic: A step towards interpretable deep learning based fuzzy classifiers

Interpreting Variational Autoencoders with Fuzzy Logic: A st...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Bolat, Kutay Kumbasar, Tufan Istanbul Tech Univ Dept Control & Automat Engn Istanbul Turkey

ISBN: (纸本)9781728169323

The emerging success of Deep Learning (DL) in various application areas comes also with the questions starting with "How"s and "Why"s. These questions can be answered if the DL methods are interpretable and thus provide a certain a degree of explanation. In this paper, we propose a DL framework that leverages the advantages of beta-variational autoencoder (VAE) and Fuzzy Sets (FSs), which are disentanglement and linguistic representation, for the design of a novel DL based Fuzzy Classifier (FC). We first present a step-by-step design approach to construct the DL-FC which is composed of the encoder layer of beta-VAE and a Fuzzy Logic System (FLS) followed by a softmax layer. The beta-VAE is trained so that the semantic information of the high dimensional data is captured. The latent space of the beta-VAE is clustered to extract FSs. The FSs are then used to define antecedents of the FLS that is trained with DL methods. We present results conducted on the MNIST dataset and showed that DL-FC is quite competitive with its deep neural network counterpart. We then try to provide an interpretation to the antecedents of FLS by examining the FSs, the latent traversals and heat-maps of each latent dimension. The results show that the antecedents of FLS can be defined with linguistic interpretations. Thus, for the first time in the literature, we showed that linguistic interpretations can be defined for the latent space of beta-VAE with FSs.

关键词： variational autoencoder fuzzy sets fuzzy c-means clustering classification interpretation

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Sequential Segment-based Level Generation and Blending using variational autoencoders 20

Sequential Segment-based Level Generation and Blending using...

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15th International Conference on the Foundations of Digital Games

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

ISBN: (纸本)9781450388078

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

关键词： procedural content generation variational autoencoder level generation level blending game blending PCGML

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Data Augmentation and Recognition for Plunger Lift Based on variational autoencoders 6

Data Augmentation and Recognition for Plunger Lift Based on ...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Xu, Zhiyu Lin, Hai Jia, Yujiang Du, Jing Mao, Jianjun Zhu, Jianjun Chen, Jun Li, Fangming Li, Nan Petro China Qinghai Oilfield Dunhuang China China University of Petroleum Beijing China Research Institute of Petroleum Exploration & Development Beijing China

ISBN: (纸本)9798331516550

Plunger lift technology effectively addresses liquid loading issues in the later stages of natural gas well production. With the continuous development of artificial intelligence technology, the rapid identification of plunger data has become a challenge in field operations. Due to the lack of labeled field data and significant data imbalance, it is difficult to directly identify using discriminative models. This paper proposes a plunger lift generative model based on variational autoencoders (VAEs) for data augmentation of pressure data in plunger lift processes. The model maps original data through an encoder to obtain a high-dimensional latent variable probability distribution;the decoder maps the latent variable back to the real space to generate samples. The generated data supplements the original data, which is then input into a discriminative network for training. The results show that compared to the original discriminative model, the model's performance indicators have improved after data augmentation. © 2024 IEEE.

关键词： Liquid loading Plunger lift variational autoencoder

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Differentially Private Synthetic Data Generation via Lipschitz-Regularised variational autoencoders 9

Differentially Private Synthetic Data Generation via Lipschi...

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9th IEEE Smart World Congress, SWC 2023

作者： Gros, Benedikt Wunder, Gerhard Freie Universität Department of Computer Science Berlin Germany

ISBN: (纸本)9798350319804

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments. © 2023 IEEE.

关键词： differential privacy Lipschitz regularisation variational autoencoder

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Learning Physically Meaningful Representations of Energy Systems with variational autoencoders 27

Learning Physically Meaningful Representations of Energy Sys...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Multaheb, Samim Bauer, Fabian Bretschneider, Peter Niggemann, Oliver Helmut Schmidt Univ Inst Automat Technol Hamburg Germany Ilmenau Univ Technol Inst Elect Power & Control Engn Ilmenau Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.

关键词： Representation Learning Neural Networks variational autoencoder Explainable Artificial Intelligence Informed Machine Learning

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TRANSFER LEARNING FROM SYNTHETIC TO REAL IMAGES USING variational autoencoderS FOR PRECISE POSITION DETECTION 25

TRANSFER LEARNING FROM SYNTHETIC TO REAL IMAGES USING VARIAT...

引用

25th IEEE International Conference on Image Processing (ICIP)

作者： Inoue, Tadanobu Chaudhury, Subhajit De Magistris, Giovanni Dasgupta, Sakyasingha IBM Res Tokyo Japan Ascent Robot Inc Tokyo Japan

ISBN: (纸本)9781479970612

Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variational autoencoders. Additionally, the proposed method consistently performed well in different lighting conditions, in the presence of other distractor objects, and on different backgrounds. Experimental results showed that it achieved accuracy of 1.5 mm to 3.5 mm on average. Furthermore, we showed how the method can be used in a real-world scenario like a "pick-and-place" robotic task.

关键词： deep learning position detection transfer learning variational autoencoder computer simulation

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Unsupervised Wafermap Patterns Clustering via variational autoencoders

Unsupervised Wafermap Patterns Clustering via Variational Au...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tulala, Peter Mahyar, Hamidreza Ghalebi, Elahe Grosu, Radu Vienna Univ Technol TU Wien Inst Comp Engn Vienna Austria

ISBN: (纸本)9781509060146

Semiconductor manufacturing processes are prone to process deviations or other production issues. Quality assurance of every processing step and measuring wafer test values is crucial for finding possible root causes of these problems. Automated visual inspection and recognition of patterns in wafermap data obtained during different processing steps has a potential to signifficantly improve the efficiency of finding early production issues and even help with adjustment of the production parameters to automatically resolve them. In this paper, we present a machine learning approach for unsupervised clustering of spatial patterns in wafermap measurement data. Measured test values are first pre-processed using some computer vision techniques, followed by a feature extraction based on variational autoencoders to decompose high-dimensional wafermaps into a low-dimensional latent representation. Final step is to detect the structure of this latent space and assign individual wafers into clusters. We experimentally evaluate the performance of the proposed method over a real dataset.

关键词： Wafer test data Image processing Semiconductor anufacturing Pattern recognition variational autoencoder Clustering

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CONTINUAL LEARNING OF PREDICTIVE MODELS IN VIDEO SEQUENCES VIA variational autoencoderS

CONTINUAL LEARNING OF PREDICTIVE MODELS IN VIDEO SEQUENCES V...

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IEEE International Conference on Image Processing (ICIP)

作者： Campo, Damian Slavic, Giulia Baydoun, Mohamad Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Genoa Italy

ISBN: (纸本)9781728163956

This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial variational autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment.

关键词： Continual learning lifelong learning variational autoencoder particle filter kalman filter

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Fast and Efficient Speech Enhancement with variational autoencoders 48

Fast and Efficient Speech Enhancement with Variational Autoe...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sadeghi, Mostafa Serizel, Romain Université de Lorraine Cnrs Inria Loria NancyF-54000 France

ISBN: (纸本)9781728163277

Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use of a pre-trained deep speech prior along with a parametric noise model, where the noise parameters are learned from the noisy speech signal with an expectation-maximization (EM)-based method. The E-step involves an intractable latent posterior distribution. Existing algorithms to solve this step are either based on computationally heavy Monte Carlo Markov Chain sampling methods and variational inference, or inefficient optimization-based methods. In this paper, we propose a new approach based on Langevin dynamics that generates multiple sequences of samples and comes with a total variation-based regularization to incorporate temporal correlations of latent vectors. Our experiments demonstrate that the developed framework makes an effective compromise between computational efficiency and enhancement quality, and outperforms existing methods. © 2023 IEEE.

关键词： deep generative model Langevin dynamics Speech enhancement variational autoencoder

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Rolling Bearing Fault Diagnosis Based on Multi-Modal variational autoencoders 3

Rolling Bearing Fault Diagnosis Based on Multi-Modal Variati...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xiong, Manjun Wu, Yifan Li, Chuan Yang, Zhe School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China Chongqing Technology and Business University Research Center of System Health Maintenance Chongqing400067 China School of Mechanical Engineering Dongguan University of Technology and Business University Dongguan523808 China

ISBN: (纸本)9781665492812

With the development of Industry 4.0, more and more attention has been paid to system intelligent maintenance by various industries, among which rolling bearing is an indispensable and most important component. Existing methods have such limitations as the need for prior knowledge and manual feature extraction. For this reason, a multi-modal variational autoencoder (MMVAE) is proposed to extract useful features from multiple modalities. Firstly, the fault characteristics of multiple modalities are extracted separately by different variational autoencoders containing complementary information. Secondly, a collaborative training method is proposed to maximize mutual consistency. Specifically, feature extraction and clustering for all modalities are employed for collaborative learning. Fault diagnosis experiments on a benchmark rolling bearing dataset were carried out. Compared with other methods, MMVAE showed remarkable results, with an accuracy of 99.13%. © 2022 IEEE.

关键词： fault diagnosis intelligent computing multi-modality rolling bearing variational autoencoder

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