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Oak Ridge National Lab7mon
Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults
A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...
IEEE1y
Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder
To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of ...
GitHub2y
convolutional-autoencoder
This project is a practice implementation of an autoencoder, The primary use case for this autoencoder is for anomaly detection in sales data, but it can be adapted for other purposes. The autoencoder ...
Frontiers3y
Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit
Convolutional autoencoder and shallow classifiers ... filters in each convolutional layer increases by a factor of 2 from outside to the inside of the encoder, the kernel size of the convolutional ...
Frontiers3y
Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets
There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and ...
unite4y
What is an Autoencoder?
Similar to convolution neural networks, a convolutional autoencoder specializes in the learning of image data, and it uses a filter that is moved across the entire image section by section. The ...