However, most existing autoencoder-based methods discard the reconstruction of auxiliary information, which poses a huge challenge for better representation learning and model scalability.

A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen Graduate School has developed a ...

After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning ...