Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between 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 …

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its
ability to detect environmental contaminants with high sensitivity and specificity. The cost …

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized
semantic predictions across diverse urban-scene styles. Unlike generic domain gap …

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD)
data, especially in the extreme case of single domain generalization (single-DG) that …

In domain generalization (DG), most existing methods aspire to fine-tune a specific
pretrained model through novel DG algorithms. In this paper, we propose an alternative …

The annotation scarcity of medical image segmentation poses challenges in collecting
sufficient training data for deep learning models. Specifically, models trained on limited data …

In the context of single domain generalisation, the objective is for models that have been
exclusively trained on data from a single domain to demonstrate strong performance when …

In this paper, we present three major challenges in occluded person Re-Identification
(ReID): different occlusions, background interference, and dataset bias. To address the first …

As black-box models increasingly power high-stakes applications, a variety of data-driven
explanation methods have been introduced. Meanwhile, machine learning models are …