Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

Machine-learning models are key to modern biology, yet models trained on one dataset are
often not generalizable to other datasets from different cohorts or laboratories due to both …

In this paper, we propose a novel domain invariant feature learning (DIFL) method to deal
with speaker-independent speech emotion recognition (SER). The basic idea of DIFL is to …

In many practical applications, it is often difficult and expensive to obtain enough large-scale
labeled data to train deep neural networks to their full capability. Therefore, transferring the …

Most existing domain adaptation methods focus on adaptation from only one source domain,
however, in practice there are a number of relevant sources that could be leveraged to help …

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on
condition that source and target data are identically distributed. In engineering practice …

Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network
to extract domain-invariant features. However, when facing multiple source domains …

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection
of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning …

Learning from multiple modalities has recently attracted increasing attention in many tasks.
However, deep learning-based multi-modal learning cannot guarantee good generalization …

Abstract Domain adaptation (DA) methods facilitate cross-domain learning by minimizing the
marginal or conditional distribution shift between domains. However, the conditional …