Domain adaptation without source data
Domain adaptation assumes that samples from source and target domains are freely
accessible during a training phase. However, such an assumption is rarely plausible in the …
accessible during a training phase. However, such an assumption is rarely plausible in the …
Trust your good friends: Source-free domain adaptation by reciprocal neighborhood clustering
Domain adaptation (DA) aims to alleviate the domain shift between source domain and
target domain. Most DA methods require access to the source data, but often that is not …
target domain. Most DA methods require access to the source data, but often that is not …
Exploiting the intrinsic neighborhood structure for source-free domain adaptation
S Yang, J Van de Weijer… - Advances in neural …, 2021 - proceedings.neurips.cc
Abstract Domain adaptation (DA) aims to alleviate the domain shift between source domain
and target domain. Most DA methods require access to the source data, but often that is not …
and target domain. Most DA methods require access to the source data, but often that is not …
Source-free domain adaptation via distribution estimation
Abstract Domain Adaptation aims to transfer the knowledge learned from a labeled source
domain to an unlabeled target domain whose data distributions are different. However, the …
domain to an unlabeled target domain whose data distributions are different. However, the …
Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation
Due to privacy concerns and data transmission issues, Source-free Unsupervised Domain
Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than …
Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than …
A comprehensive survey on source-free domain adaptation
Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …
learning which aims to improve performance on target domains by leveraging knowledge …
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 …
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 …
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 …