Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Infinite numbers of real-world applications use Machine Learning (ML) techniques to
develop potentially the best data available for the users. Transfer learning (TL), one of the …

近年来, 迁移学习已经引起了广泛的关注和研究. 迁移学习是运用已存有的知识对不同但相关
领域问题进行求解的一种新的机器学习方法. 它放宽了传统机器学习中的两个基本假设:(1) …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

With the rapid development of computing technology, wearable devices make it easy to get
access to people's health information. Smart healthcare achieves great success by training …

Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …

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 …