Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

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 …

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …

Abstract Machine learning algorithms typically assume that training and test examples are
drawn from the same distribution. However, distribution shift is a common problem in real …

Abstract Domain generalization refers to the problem of training a model from a collection of
different source domains that can directly generalize to the unseen target domains. A …

Text information including extensive prior knowledge about land cover classes has been
ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the …

For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …

AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making
the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its …

Generalizing the medical image segmentation algorithms to unseen domains is an important
research topic for computer-aided diagnosis and surgery. Most existing methods require a …

Abstract Domain generalization (DG) aims to learn a robust model from source domains that
generalize well on unseen target domains. Recent studies focus on generating novel …