Speech feature learning is the key to speech mental health recognition. Deep feature learning can automatically extract the speech features but suffers from the small sample problem. The traditional ...

Speech feature learning is the key to speech mental health recognition. Deep feature learning can automatically extract the speech features but suffers from the small sample problem. The traditional ...

A complete end-to-end pipeline from activation capture to Sparse AutoEncoder (SAE) training, feature interpretation, and verification, written in pure PyTorch with minimal dependencies. Specifically: ...

This repository contains PyTorch implementation of sparse autoencoder and it's application for image denosing and reconstruction. Autoencoder (AE) is an unsupervised deep learning algorithm, capable ...

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 ...