Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
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 …
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …
Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
Unsupervised multi-source domain adaptation without access to source data
Abstract Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an
unlabeled dataset by transferring knowledge from a labeled source data, which has been …
unlabeled dataset by transferring knowledge from a labeled source data, which has been …
Bayesian uncertainty matching for unsupervised domain adaptation
Domain adaptation is an important technique to alleviate performance degradation caused
by domain shift, eg, when training and test data come from different domains. Most existing …
by domain shift, eg, when training and test data come from different domains. Most existing …
Source-free unsupervised domain adaptation: A survey
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …
for tackling domain-shift problems caused by distribution discrepancy across different …
Clda: Contrastive learning for semi-supervised domain adaptation
A Singh - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
Abstract Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant predictive …
distribution with the unlabeled target distribution to obtain domain invariant predictive …
[PDF][PDF] Unsupervised domain adaptation without source data by casting a bait
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a
labeled source domain to an unlabeled target domain. Existing UDA methods require …
labeled source domain to an unlabeled target domain. Existing UDA methods require …
A prototype-oriented framework for unsupervised domain adaptation
K Tanwisuth, X Fan, H Zheng… - Advances in …, 2021 - proceedings.neurips.cc
Existing methods for unsupervised domain adaptation often rely on minimizing some
statistical distance between the source and target samples in the latent space. To avoid the …
statistical distance between the source and target samples in the latent space. To avoid the …
Partial disentanglement for domain adaptation
Unsupervised domain adaptation is critical to many real-world applications where label
information is unavailable in the target domain. In general, without further assumptions, the …
information is unavailable in the target domain. In general, without further assumptions, the …
Source-free domain adaptation via distributional alignment by matching batch normalization statistics
M Ishii, M Sugiyama - arXiv preprint arXiv:2101.10842, 2021 - arxiv.org
In this paper, we propose a novel domain adaptation method for the source-free setting. In
this setting, we cannot access source data during adaptation, while unlabeled target data …
this setting, we cannot access source data during adaptation, while unlabeled target data …