The project is divided into three main parts: Generating a noisy sinusoidal signal as input. Training a linear autoencoder to perform PCA-like transformation. Visualizing reconstructed signals and ...

Utilizes the MNIST dataset for training and evaluation. Defines both a simple and multilayer customizable autoencoder architecture. Includes training and evaluation scripts. Encoder: A single linear ...

Abstract: This paper presents a novel structure for data-driven fault detection using multi-layer autoencoder whose hidden layer trajectory is embedded into a linear space. The hidden layer is ...

Most of the existing thermographic data analysis methods are supervised and/or linear ... a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite ...

The linear dimension reduction transformation \(\mathbf{w ... By introducing the dimension reduction layer in the autoencoder structure based on deep learning, we can extend the current DNN framework ...

Soil structure interaction (SSI ... We exploit a data-driven non-linear ROM methodology coupling an autoencoder with long short-term memory (LSTM) neural networks. The ROM is trained to emulate a ...

This ability does not increase further with more complex model structure as shown by the 5-layer VAE. TABLE 3. Variational autoencoder evaluation using MMD and EMD metrics. Variational autoencoder ...