Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack
performance when faced with out-of-distribution data, a common scenario due to the …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …

To calculate the model accuracy on a computer vision task, eg, object recognition, we
usually require a test set composing of test samples and their ground truth labels. Whilst …

Deep learning has achieved tremendous success in recent years, but most of these
successes are built on an independent and identically distributed (IID) assumption. This …

In this paper, we consider the problem of domain generalization in semantic segmentation,
which aims to learn a robust model using only labeled synthetic (source) data. The model is …

Domain generalization aims to enhance the model robustness against domain shift without
accessing the target domain. Since the available source domains for training are limited …

Single domain generalization challenges model generalizability to unseen target domains
when only one source domain is provided for training. To tackle this problem, domain …