Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

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 …

The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain
configurations, including closed-set and partial-set DA, as well as multi-source and multi …

Unsupervised domain adaptation (DA) has gained substantial interest in semantic
segmentation. However, almost all prior arts assume concurrent access to both labeled …

We introduce D omain-specific M asks for G eneralization, a model for improving both in-
domain and out-of-domain generalization performance. For domain generalization, the goal …

Federated learning improves data privacy and efficiency in machine learning performed
over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc …