Search Results - supervised conditional variational autoencoder (VAE)

Semi-Supervised Deep Conditional Variational Autoencoder for Soft Sensor Modeling by Tang, Xiaochu, Yan, Jiawei, Li, Yuan, Zhang, Xinmin, Song, Zhihuan

“…Variational autoencoder (VAE) as an unsupervised deep generated model has been widely applied to process modeling for industrial processes due to its excellent ability in nonlinear and uncertain feature extraction…”
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Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions by Zhou, Haoxuan, Lei, Zihao, Zio, Enrico, Wen, Guangrui, Liu, Zimin, Su, Yu, Chen, Xuefeng

“…•A Conditional VAE network combined with PCA is developed to realize the feature disentanglement and AD…”
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SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder by Zheng, Dihan, Zou, Yihang, Zhang, Xiaowen, Bao, Chenglong

“… This study proposes SeNM-VAE, a semi-supervised noise modeling method that leverages both paired and un-paired datasets to generate realistic degraded data…”
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Supervised Determined Source Separation with Multichannel Variational Autoencoder by Kameoka, Hirokazu, Li, Li, Inoue, Shota, Makino, Shoji

“…This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE…”
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Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation by Lavda, Frantzeska, Kalousis, Alexandros

“… To address these issues, we propose BtVAE, a method that utilizes conditional VAE models to achieve combinatorial generalization in certain scenarios and consequently to generate out-of-distribution (OOD…”
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Generating functional protein variants with variational autoencoders by Hawkins-Hooker, Alex, Depardieu, Florence, Baur, Sebastien, Couairon, Guillaume, Chen, Arthur, Bikard, David

“… in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases…”
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Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data by Matsuo, Hidetoshi, Kitajima, Kazuhiro, Kono, Atsushi K., Kuribayashi, Kozo, Kijima, Takashi, Hashimoto, Masaki, Hasegawa, Seiki, Yamakado, Koichiro, Murakami, Takamichi

“… Methods A 3D convolutional conditional variational autoencoder (3D‐CCVAE), which adds a 3D‐convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images…”
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Chiller Fault Diagnosis Based on VAE-Enabled Generative Adversarial Networks by Yan, Ke, Su, Jianye, Huang, Jing, Mo, Yuchang

“… In this study, a variational autoencoder-based conditional Wasserstein GAN with gradient penalty (CWGAN-GP-VAE…”
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Small-data image classification via drop-in variational autoencoder by Mahdian, Babak, Nedbal, Radim

“… In this paper, we propose a drop-in variational autoencoder (VAE) for the task of supervised learning using an extremely small train set (i.e., n = 1…”
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Conditional Generative Denoising Autoencoder by Karatsiolis, Savvas, Schizas, Christos N.

“…We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples…”
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Classification of Expert-Novice Level Using Eye Tracking And Motion Data via Conditional Multimodal Variational Autoencoder by Akamatsu, Yusuke, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki

“… Our proposed anomaly detection model named conditional multimodal variational autoencoder (CMVAE) has the following two technical contributions…”
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Joint Disentanglement of Labels and Their Features with VAE by Zou, Kaifeng, Faisan, Sylvain, Heitz, Fabrice, Valette, Sebastien

“…Most of previous semi-supervised methods that seek to obtain disentangled representations using variational autoencoders divide the latent representation into two components…”
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Variational AutoEncoder For Regression: Application to Brain Aging Analysis by Zhao, Qingyu, Adeli, Ehsan, Honnorat, Nicolas, Leng, Tuo, Pohl, Kilian M

“…While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored…”
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Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders by Valleti, Mani, Ziatdinov, Maxim, Liu, Yongtao, Kalinin, Sergei V.

“… and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs…”
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Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints by Yasutomi, Suguru, Tanaka, Toshihisa

“… Unsupervised methods such as variational autoencoders (VAEs) can extract styles that are usually mixed with other features. Conditional VAEs (CVAEs…”
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Solving two-stage stochastic integer programs via representation learning by Wu, Yaoxin, Cao, Zhiguang, Song, Wen, Zhang, Yingqian

“… To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning…”
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SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder by Zheng, Dihan, Zou, Yihang, Zhang, Xiaowen, Bao, Chenglong

“… This study proposes SeNM-VAE, a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate realistic degraded data…”
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Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization by Idoko, Simon, Sharma, Basant, Singh, Arun Kumar

“… Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE…”
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Latent Combinational Game Design by Sarkar, Anurag, Cooper, Seth

“… We use Gaussian mixture variational autoencoders (GMVAEs), which model the VAE latent space via a mixture of Gaussian components…”
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VAESim: A probabilistic approach for self-supervised prototype discovery by Ferrante, Matteo, Boccato, Tommaso, Spasov, Simeon, Duggento, Andrea, Toschi, Nicola

“… In this work, we propose VAESim, an architecture for image stratification based on a conditional variational autoencoder…”
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