In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still ...

In this paper, the stacked convolutional autoencoder network structure constructed with fusion selection kernel attention mechanism is based on FCAE, which consists of an encoder and a decoder. It can ...

Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer. HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the ...

An autoencoder is a type of artificial neural network used for unsupervised learning of efficient data codings. The aim of an autoencoder is to learn a representation (encoding) for a set of data, ...

Stacked Autoencoder-Based Probabilistic Feature Extraction for On-Device Network Intrusion Detection Abstract: Due to the outbreak of recent network attacks, it is necessary to develop a robust ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.