A basic autoencoder consists of two parts: an encoder and a decoder. The encoder takes the input data and transforms it into a lower-dimensional representation, called the latent code or the ...

As previously mentioned an autoencoder can essentially be divided up into three different components: the encoder, a bottleneck, and the decoder. The encoder portion of the autoencoder is typically a ...

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the ...

What is an LSTM autoencoder? LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a ...

Abstract: This paper proposes an autoencoder (AE) framework with transformer encoder and extended multilinear mixing model (EMLM) embedded decoder for nonlinear hyperspectral anomaly detection.

In this project, we train an autoencoder for information transmission over an end-to-end communication system, where the encoder will replace the transmitter tasks such as modulation and coding along ...

Autoencoder sind eine Art künstliches neuronales Netz, das lernen kann, Daten unbeaufsichtigt zu kodieren und zu dekodieren. Sie können für Aufgaben wie die Erkennung von Anomalien und die ...

As previously mentioned an autoencoder can essentially be divided up into three different components: the encoder, a bottleneck, and the decoder. The encoder portion of the autoencoder is typically a ...