Search Results for author:
Found 3 papers, 2 papers with code
Federated Adversarial Domain Adaptation
In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node.
Domain Agnostic Learning with Disentangled Representations
Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains.
Ranked #4 on
Multi-target Domain Adaptation
on DomainNet
Moment Matching for Multi-Source Domain Adaptation
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
