Builds autoencoder model using Pytorch module. The outputs of the encoder and decoder structures are compared to demonstrate the models ability to reconstructs the input image, as well as decoder's ...

Improved Autoencoder Model With Memory Module for Anomaly Detection (IAEMM) is an unsupervised anomaly detection algorithm that enhances traditional autoencoders with a memory module and a hypersphere ...

Abstract: Variational autoencoder (VAE) is widely used as a data enhancement technique. However, it faces challenges with inaccurate potential spatial distribution and poor reconstruction quality when ...

Abstract: In this paper, we propose a novel Transformer based approach, namely Cross-modal Contrastive Masked AutoEncoder (C2MAE), to Self-Supervised Learning (SSL) on compressed videos. A unified ...

Traditional data-driven models for predicting rare earth component content are primarily developed by relying on supervised learning methods, which suffer from limitations such as a lack of labeled ...

The growing digitalization of Industrial Control Systems (ICSs) presents both significant benefits and security challenges, especially for small and medium-sized factories with limited resources.