In the last decade, automatic writer identification using a convolutional neural network (CNN) has been well studied. For further performance improvement of the writer identification task, a ...

A multifunction radar working mode open-set recognition method based on dual autoencoder semantic feature fusion (DASFF) was proposed to address the challenge of effectively distinguishing unknown ...

The autoencoder is an unsupervised deep neural network that learns a compressed representation from the input data and reconstructs an output that is as similar as possible to the original data.

Fig. 4: Attention Autoencoder anomaly scores with time-domain MSE loss The plots show how effectively each model distinguishes between normal and faulty bearing conditions. The Attention Autoencoder ...

Image Autoencoder This project implements a simple convolutional autoencoder built in PyTorch for image reconstruction and learned compression. The objective was to build a simple, flexible framework ...

Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs’ roles in complex diseases, providing a theoretical basis for disease mechanisms.