In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start
with reviewing the history of kernels in functional analysis and machine learning. Then …

This paper addresses the problem of ranking pre-trained models for object detection and
image classification. Selecting the best pre-trained model by fine-tuning is an expensive and …

A main goal in the field of statistical shape analysis is to define computable and informative
metrics on spaces of immersed manifolds, such as the space of curves in a Euclidean space …

Siamese neural network is a very powerful architecture for both feature extraction and metric
learning. It usually consists of several networks that share weights. The Siamese concept is …

Distance learning has spread nowadays on a large scale across the world, which has led to
many challenges in education such as invigilation and learning coordination. These …

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 …

This paper is a tutorial and literature review on sampling algorithms. We have two main
types of sampling in statistics. The first type is survey sampling which draws samples from a …

Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional
discrepancies between labeled source and the unlabeled target domain (TD) in machine …

Single-cell trajectory mapping and spatial reconstruction are two important developments in
life science and provide a unique means to decode heterogeneous tissue formation, cellular …