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 …

The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …

Partially-supervised multi-organ medical image segmentation aims to develop a unified
semantic segmentation model by utilizing multiple partially-labeled datasets, with each …

Abstract Remaining Useful Life (RUL) predictions play vital role for asset planning and
maintenance leading to many benefits to industries such as reduced downtime, low …

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous
applications in the real world, particularly with respect to data privacy. Existing approaches …

Abstract Representation learning (RL) aims to extract latent features from various types of
data and then facilitate a wide range of downstream data analytics tasks, such as …

Source-free unsupervised domain adaptation (SFUDA) seeks to adapt a pre-trained model
from the source domain to the unlabeled target domain without using any source domain …

Recent advances in pre-trained vision-language models, such as CLIP, have demonstrated
remarkable success in domain adaptation (DA) by tuning prompts. To promote DA, one …

Pretrained machine learning models need to be adapted to distribution shifts when
deployed in new target environments. When obtaining labeled data from the target …