Výsledky vyhledávání - "Adversarial variational autoencoder"

Coupled adversarial variational autoencoder Autor Hou, Yingzhen, Zhai, Junhai, Chen, Jiankai

“… To address this problem, we proposed a novel model which consists of two adversarial variational autoencoders, each one aim at generating an image of pairs more accurately…”
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A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis Autor Lin, Eugene, Mukherjee, Sudipto, Kannan, Sreeram

“…-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction…”
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adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection Autor Wang, Xuhong, Du, Ying, Lin, Shijie, Cui, Ping, Shen, Yuntian, Yang, Yupu

“… To solve the above two problems, we propose a self-adversarial variational autoencoder (adVAE) with a Gaussian anomaly prior assumption…”
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Interpretable data-augmented adversarial variational autoencoder with sequential attention for imbalanced fault diagnosis Autor Liu, Yunpeng, Jiang, Hongkai, Yao, Renhe, Zhu, Hongxuan

“… In this paper, an interpretable data-augmented adversarial variational autoencoder with sequential attention (AVAE-SQA…”
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Semi-Supervised Adversarial Variational Autoencoder Autor Zemouri, Ryad

“…We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead…”
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Unsupervised domain adaptation with Joint Adversarial Variational AutoEncoder Autor Li, Yuze, Zhang, Yan, Yang, Chunling

“… In this paper, we propose a Joint Adversarial Variational AutoEncoder (JVA2E) for unsupervised domain adaptation tasks…”
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Fault Detection and Identification of Underwater Thruster Without Fault Sample Based on Adversarial Variational Autoencoder Autor Zhu, Daqi, Zeng, Tianhong

“…This article proposes a new underwater thruster fault detection and identification method based on adversarial variational autoencoder (AdvVAE…”
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Self-adversarial variational autoencoder with spectral residual for time series anomaly detection Autor Liu, Yunxiao, Lin, Youfang, Xiao, QinFeng, Hu, Ganghui, Wang, Jing

“… the performance of detection methods. To this end, a novel unsupervised method, called Self-adversarial Variational Autoencoder with Spectral Residual (SaVAE-SR…”
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An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices Autor Salsabilian, Shiva, Lee, Christian, Margolis, David, Najafizadeh, Laleh

“… To address subject variability in decoding neural activity, adversarial learning in combination with a long short-term memory-based adversarial variational autoencoder…”
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Deceptive reviewer group detection using self-adversarial variational autoencoder: a heterogeneous graph-based approach Autor Maurya, Sushil Kumar, Singh, Dinesh

“…With the rapid growth of online review platforms and e-commerce websites, user-posted reviews have become an essential factor influencing consumers' purchasing…”
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Infer-AVAE: An attribute inference model based on adversarial variational autoencoder Autor Zhou, Yadong, Ding, Zhihao, Liu, Xiaoming, Shen, Chao, Tong, Lingling, Guan, Xiaohong

“…User attributes, such as gender and education, face severe incompleteness in social networks. Attribute inference aims to infer users’ missing attribute labels…”
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Advancing Metaverse-Based Healthcare With Multimodal Neuroimaging Fusion Via Multi-Task Adversarial Variational Autoencoder for Brain Age Estimation Autor Usman, Muhammad, Rehman, Azka, Shahid, Abdullah, Rehman, Abd Ur, Gho, Sung-Min, Lee, Aleum, Khan, Tariq M., Razzak, Imran

“… To address these challenges, we present the Multitask Adversarial Variational Autoencoder (M-AVAE), a bespoke deep learning framework…”
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Adversarial variational autoencoder for attributed graph embedding with high-frequency noise filtering Autor Zhou, Hui, Liu, Xin, Li, Xiangju, Zhao, Zhongying, Li, Chao

“…Attributed graph embedding aims to learn low-dimensional representations by fully exploiting both topological structure and rich attributes. The embeddings…”
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CM-AVAE: Cross-Modal Adversarial Variational Autoencoder for Visual-to-Tactile Data Generation Autor Xi, Qiyuan, Wang, Fei, Tao, Liangze, Zhang, Hanjing, Jiang, Xun, Wu, Juan

“… Furthermore, this approach can be applied to robot-related tasks. This letter presents a cross-modal adversarial variational autoencoder (CM-AVAE…”
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Multi-adversarial Variational Autoencoder Networks Autor Imran, Abdullah-Al-Zubaer, Terzopoulos, Demetri

“… Combining the power of these two generative models, we introduce a novel network architecture, Multi-Adversarial Variational autoEncoder Networks (MAVENs…”
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Speech Emotion Recognition based on Semi-Supervised Adversarial Variational Autoencoder Autor Xiao, Yufeng, Bo, Yuqin, Zheng, Zhiling

“…The application of semi-supervised learning in the field of speech emotion recognition can alleviate the problem of dependence on the amount of labeled data…”
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Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE Autor Zhao, Siliang, Luo, Wuman, Shu, Qin, Xu, Fangwei

“… To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE…”
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DAVA: Disentangling Adversarial Variational Autoencoder Autor Estermann, Benjamin, Wattenhofer, Roger

“…The use of well-disentangled representations offers many advantages for downstream tasks, e.g. an increased sample efficiency, or better interpretability…”
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Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design Autor Koike-Akino, Toshiaki, Jung, Minwoo, Chakrabarty, Ankush, Wang, Ye, Kojima, Keisuke, Brand, Matthew

“…We propose a device design framework based on Bayesian optimization for efficient latent sampling of adversarial generative neural networks to expedite inverse…”
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Transformer-based Denoising Adversarial Variational Entity Resolution Autor Li, Shuaichao, Wu, Huaiguang

“…Entity resolution (ER), precisely identifying different representations of the same real-world entities, is critical for data integration. The ER question has…”
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