An autoencoder is an unsupervised learning model designed to encode input data into a lower-dimensional feature representation through the encoder, and then reconstruct the original input data as ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

The model can then be trained to generate audio in this space and upsample back to the raw audio space. Jukebox’s autoencoder model processes audio with an approach called Vector Quantized ...

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 ...