In order to minimize inference time and computational energy, a convolutional autoencoder is used for learning a generalized representation of the images. Three scenarios are analyzed: transferring ...

To make our anomaly detection lightweight, we further design a Light Convolutional Autoencoder (LightCAE) which contains a compressed autoencoder by exploiting tensor factorization to largely compress ...

Convolutional Autoencoder with LSTM for CFD Predictions This repository contains a machine learning model that combines a Convolutional Autoencoder (CAE) and Long-Short Term Memory (LSTM) network to ...

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 ...

These results indicate that the introduced 2D convolutional autoencoder and multi-sequence, multi-scale asynchronous information extraction methods effectively capture asynchronous correlation ...

In this paper, through the experimental comparison of multi-layer perceptron, convolutional neural network, and the proposed convolutional autoencoder, we find that the constructed convolutional ...