The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

Supervised learning techniques have shown substantial progress on video summarization.
State-of-the-art approaches mostly regard the predicted summary and the human summary …

Distance measure plays a critical role in various applications of polarimetric synthetic
aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have …

The inverse design of tailored organic molecules for specific optoelectronic devices of high
complexity holds an enormous potential but has not yet been realized. Current models rely …

This study presents a novel artificial neural network (ANN) based methodology within a
vibration-based structural health monitoring framework for robust damage detection. The …

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer
learning, with a particular focus placed on domain adaptation from a theoretical point-of …

Robust few-shot learning (RFSL), which aims to address noisy labels in few-shot learning,
has recently gained considerable attention. Existing RFSL methods are based on the …

Feature selection is essential in the analysis of molecular systems and many other fields, but
several uncertainties remain: What is the optimal number of features for a simplified …