Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …

In recent years, deep neural networks have emerged as a dominant machine learning tool
for a wide variety of application domains. However, training a deep neural network requires …

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 …

Deep neural networks suffer from performance decay when there is domain shift between
the labeled source domain and unlabeled target domain, which motivates the research on …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity
labels. To solve this problem, transfer learning leverages the labeled samples from the …

Simulation-to-real domain adaptation for semantic segmentation has been actively studied
for various applications such as autonomous driving. Existing methods mainly focus on a …

With the rapid development of the Internet industry, sentiment analysis has grown into one of
the popular areas of natural language processing (NLP). Through it, the implicit emotion in …

In this paper, we are interested in pose estimation of animals. Animals usually exhibit a wide
range of variations on poses and there is no available animal pose dataset for training and …