We consider learning representations (features) in the setting in which we have access to
multiple unlabeled views of the data for representation learning while only one view is …

Long training times for high-accuracy deep neural networks (DNNs) impede research into
new DNN architectures and slow the development of high-accuracy DNNs. In this paper we …

This paper presents a new discriminative deep metric learning (DDML) method for face and
kinship verification in wild conditions. While metric learning has achieved reasonably good …

Single modality action recognition on RGB or depth sequences has been extensively
explored recently. It is generally accepted that each of these two modalities has different …

The problem of extracting the relevant aspects of data was addressed through the
information bottleneck (IB) method, by (soft) clustering one variable while preserving …

A novel reference free approach for identifying structural modal parameters using recursive
canonical correlation analysis (RCCA) is proposed in this paper. Using first order eigen …

Super-resolution reconstruction of face image is the problem of reconstructing a high
resolution face image from one or more low resolution face images. Assuming that high and …

Objective. Electroencephalography (EEG) cleaning has been a longstanding issue in the
research community. In recent times, huge leaps have been made in the field, resulting in …

Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality
reduction and has been applied to image processing, pose estimation and other fields …

It has been previously shown that, when both acoustic and articulatory training data are
available, it is possible to improve phonetic recognition accuracy by learning acoustic …