The Autoencoder Decoder Repository is a comprehensive collection of code and resources dedicated to autoencoders and their associated decoders. Autoencoders are unsupervised learning models used for ...

The best autoencoder architectures for dimensionality reduction vary based on data characteristics and goals. Start with a basic autoencoder and progress to more complex architectures if needed ...

An 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 space or the bottleneck.

The core of Autoencoder is the code-decode operation. Both the encoder and decoder may be Convolutional Neural Network or fully-connected feedforward neural networks. An autoencoder has three main ...

The decoder then takes random samples from the corresponding distribution and uses them to reconstruct the initial inputs to the network. Autoencoder Applications. Autoencoders can be used for a wide ...

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 decoder. About the dataset. The ...

In this study, we propose the autoencoder reduction method (AERed), a dimensionality reduction (DR) technique based on the symmetric design of a typical autoencoder (AE). With the new structure, there ...

Owing to the immense popularity of ray-tracing and path tracing rendering algorithms for visual effects, there has been a surge of interest in developing filtering and reconstruction methods to deal ...

This study proposes two denoising autoencoder models with discrete cosine transform and discrete wavelet transform, to remove electrode motion artifacts in noisy electrocardiography. Initially, the ...

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