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Found 10 papers, 0 papers with code
Chronic pain detection from resting-state raw EEG signals using improved feature selection
We present an automatic approach that works on resting-state raw EEG data for chronic pain detection.
Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification
In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification.
Finding neural signatures for obesity through feature selection on source-localized EEG
Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life.
Variational Transfer Learning using Cross-Domain Latent Modulation
To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential.
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations
In addition, to overcome the variations in medical images, the mean-teacher mechanism is utilized as an auxiliary regularization of the discriminator.
Cross-Domain Latent Modulation for Variational Transfer Learning
Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain.
Resting-state EEG sex classification using selected brain connectivity representation
Effective analysis of EEG signals for potential clinical applications remains a challenging task.
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains.
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains.
Image segmentation with superpixel-based covariance descriptors in low-rank representation
This paper investigates the problem of image segmentation using superpixels.
