Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

In a joint vision-language space, a text feature (eg, from" a photo of a dog") could effectively
represent its relevant image features (eg, from dog photos). Also, a recent study has …

As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Abstract Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-
trained models without requiring the original data presenting practical benefits in contexts …

Federated Learning (FL) is a decentralized machine learning paradigm, in which multiple
clients collaboratively train neural networks without centralizing their local data, and hence …

Abstract Domain generalization has attracted much interest in recent years due to its
practical application scenarios, in which the model is trained using data from various source …

Domain generalization (DG) aims to learn from multiple known source domains a model that
can generalize well to unknown target domains. The existing DG methods usually exploit the …

Unsupervised domain adaptation (UDA) aims to alleviate domain shifts by transferring
relevant domain information from a fully labeled source domain to an unknown target …