Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Self-supervised learning of speech representations has been a very active research area
but most work is focused on a single domain such as read audio books for which there exist …

Intelligent fault diagnosis of rolling element bearings has made some achievements based
on the availability of massive labeled data. However, the available data from bearings used …

Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Deep transfer-learning-based diagnosis models are promising to apply diagnosis
knowledge across related machines, but from which the collected data follow different …

The varying speed can cause the significant data distribution shift of bearings, making it
difficult for deep learning-based bearing fault diagnosis models to ensure good …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …