[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
A survey on negative transfer
Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …
facilitate learning in a target domain. It is particularly useful when the target domain has very …
Domain adaptation with auxiliary target domain-oriented classifier
Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …
Cross-domain adaptive clustering for semi-supervised domain adaptation
In semi-supervised domain adaptation, a few labeled samples per class in the target domain
guide features of the remaining target samples to aggregate around them. However, the …
guide features of the remaining target samples to aggregate around them. However, the …
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 …
Transfer learning from synthetic to real lidar point cloud for semantic segmentation
Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate
data annotation constraints in various computer vision tasks such as semantic segmentation …
data annotation constraints in various computer vision tasks such as semantic segmentation …
[HTML][HTML] Semi-supervised bidirectional alignment for remote sensing cross-domain scene classification
Remote sensing (RS) image scene classification has obtained increasing attention for its
broad application prospects. Conventional fully-supervised approaches usually require a …
broad application prospects. Conventional fully-supervised approaches usually require a …
Semi-supervised domain adaptation with source label adaptation
Abstract Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with many labeled …
target data with a few labeled and lots of unlabeled target data, along with many labeled …
Learning invariant representations and risks for semi-supervised domain adaptation
The success of supervised learning crucially hinges on the assumption that training data
matches test data, which rarely holds in practice due to potential distribution shift. In light of …
matches test data, which rarely holds in practice due to potential distribution shift. In light of …
Ecacl: A holistic framework for semi-supervised domain adaptation
Abstract This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet
under-investigated research topic that aims to learn a model of good performance using …
under-investigated research topic that aims to learn a model of good performance using …