Search Results - "convolutional variational autoencoder"

Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals by Criscuolo, Sabatina, Apicella, Andrea, Prevete, Roberto, Longo, Luca

“…Electroencephalography (EEG) allows the investigation of brain activity. However, neural signals often contain artefacts, hindering signal analysis. For…”
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A multimodal deep fusion graph framework to detect social distancing violations and FCGs in pandemic surveillance by Varghese, Elizabeth B., Thampi, Sabu M.

“… when there are illumination variation and occlusion among subjects by solely relying on video data from distributed cameras…”
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Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders by Mishra, Aditya, Samin, Ahnaf Mozib, Etemad, Ali, Hashemi, Javad

“…We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data…”
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Convolutional Variational Autoencoders for Image Clustering by Nellas, Ioannis A., Tasoulis, Sotiris K., Plagianakos, Vassilis P.

“… However, the effect of the consideration of local structure during feature extraction from a Variational Autoencoder on clustering, is still an unstudied subject in the literature…”
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Few-Shot User-Adaptable Radar-Based Breath Signal Sensing by Mauro, Gianfranco, De Carlos Diez, Maria, Ott, Julius, Servadei, Lorenzo, Cuellar, Manuel P., Morales-Santos, Diego P.

“… Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space…”
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Deep Learning Approach for Epileptic Focus Localization by Daoud, Hisham, Bayoumi, Magdy

“… The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from uncontrollable seizures…”
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A Semi-Supervised Approach For Identifying Abnormal Heart Sounds Using Variational Autoencoder by Banerjee, Rohan, Ghose, Avik

“… In this paper, we propose a semi-supervised approach to solve the problem. A convolutional Variational Autoencoder (VAE…”
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Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting by Feng, Yixue, Chandio, Bramsh Q, Thomopoulos, Sophia I, Chattopadhyay, Tamoghna, Thompson, Paul M

“… In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space…”
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Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting by Feng, Yixue, Chandio, Bramsh Q., Thomopoulos, Sophia I., Chattopadhyay, Tamoghna, Thompson, Paul M.

“… In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space…”
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Towards Unsupervised Subject-Independent Speech-Based Relapse Detection in Patients with Psychosis using Variational Autoencoders by Garoufis, C., Zlatintsi, A., Filntisis, P. P., Efthymiou, N., Kalisperakis, E., Karantinos, T., Garyfalli, V., Lazaridi, M., Smyrnis, N., Maragos, P.

“… Motivated by this, in this work we propose a Convolutional Variational Autoencoder (CVAE), in order to detect and predict the appearance of relapses in patients with psychotic disorders, such as schizophrenia and bipolar disorder…”
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Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET by Hassanaly, Ravi, Brianceau, Camille, Solal, Maëlys, Colliot, Olivier, Burgos, Ninon

“… By training a deep generative model with only images from healthy subjects, the model will learn to reconstruct pseudo-healthy images…”
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Machine Learning for Human Activity Recognition Using Non-Intrusive Sensors by Karayaneva, Yordanka Lazarova

“… The current studies lack a holistic evaluation of data derived from infrared (IR) sensors including multiple layouts, sensors positions, noise analysis, multi-subject activities, and model generalisation…”
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Learning Optimal White Matter Tract Representations from Tractography using a Deep Generative Model for Population Analyses by Feng, Yixue, Bramsh Qamar Chandio, Chattopadhyay, Tamoghna, Thomopoulos, Sophia I, Owens-Walton, Conor, Jahanshad, Neda, Garyfallidis, Eleftherios, Thompson, Paul M

“…-scale population studies. We propose a robust dimensionality reduction framework for tractography, using a Convolutional Variational Autoencoder (ConvVAE…”
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