Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …

Maintenance decisions in domains such as aeronautics are becoming increasingly
dependent on being able to predict the failure of components and systems. When data …

Video summarization technologies aim to create a concise and complete synopsis by
selecting the most informative parts of the video content. Several approaches have been …

This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have
demonstrated excellent performance on hyperspectral images (HSIs). The AE can …

Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

The concept of autoencoder was originally proposed by LeCun in 1987, early works on
autoencoder were used for dimensionality reduction or feature learning. Recently, with the …