The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Zero-shot learning consists in learning how to recognize new concepts by just having a
description of them. Many sophisticated approaches have been proposed to address the …

The reinforcement learning (RL) community has made great strides in designing algorithms
capable of exceeding human performance on specific tasks. These algorithms are mostly …

As one of the most comprehensive and objective ways to describe facial expressions, the
Facial Action Coding System (FACS) has recently received significant attention. Over the …

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …

Deep networks trained on large-scale data can learn transferable features to promote
learning multiple tasks. Since deep features eventually transition from general to specific …

Most contemporary multi-task learning methods assume linear models. This setting is
considered shallow in the era of deep learning. In this paper, we present a new deep multi …