When the test data follows a different distribution from the training data, neural networks
experience domain shift. We can address this issue with domain generalization (DG), which …

In realistic scenarios, the effectiveness of Deep Neural Networks is hindered by domain shift,
where discrepancies between training (source) and testing (target) domains lead to poor …

Traditional cross-domain tasks, including domain adaptation and domain generalization,
rely heavily on training model by source domain data. With the recent advance of vision …

Deep neural networks often experience performance degradation when evaluated on
testing (target) data that exhibit different distributions compared to the training (source) data …

The single domain generalization (SDG) based on meta-learning has emerged as an
effective technique for solving the domain-shift problem. However, the inadequate match of …