Mar 15, 2020 · We propose a novel approach which simultaneously mitigates the data distribution and learns a feature representation via a common objective.
Mar 15, 2020 · We propose a novel approach which simultaneously mitigates the data distribution and learns a feature representation via a common objective.
In this paper, therefore, we propose a novel approach which simultaneously mitigates the data distribution and learns a feature representation via a common ...
Sep 10, 2021 · Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled ...
People also ask
What is unsupervised domain adaptation method?
What are the techniques of domain adaptation?
What is feature based domain adaptation?
What is the difference between supervised and unsupervised domain adaptation?
Feature matching methods aim to reduce the distribution difference by learning a new feature representation. The feature repre- sentation can be learned via ...
Jun 18, 2020 · Our proposed method also utilizes the notion of learning discriminative feature representation in minimizing the joint hypothesis error. 4.2 ...
Specifically, TJM aims to reduce the domain difference by jointly matching the features and reweighting the instances across domains in a principled ...
Missing: metric | Show results with:metric
Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain ...
Apr 23, 2023 · We propose to learn a neural network representation function to align a joint distribution and a product distribution in the representation space.
In this paper, we present moving semantic transfer network, which learn semantic representations for unlabeled tar- get samples by aligning labeled source ...