3 accepted papers
Federated Graph Learning (FGL) enables distributed clients to collaboratively train graph neural networks while strictly preserving data privacy.However, existing FGL methods implicitly assume the reliability of local graph structures and lack elastic awareness of parameter importance during model a…
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining. However, since ps…
Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks. Despite such progress, thesemodels often face challenges in real world “wild tasks” where large differ-ence between labeled training/source data and unseen test/target dataexists. In particular, su…