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Nov 21, 2022 · This study proposes an end-to-end Deep Hierarchical Optimal Transport method (DeepHOT), which aims to learn both domain-invariant and category-discriminative ...
This study proposes an end-to-end Deep Hierarchical Optimal Transport method (DeepHOT), which aims to learn both domain-invariant and category-discriminative ...
Sep 30, 2024 · Abstract—Unsupervised domain adaptation (UDA) aims to estimate a transferable model for unlabeled target domains.
This paper deals with the problem of unsupervised domain adaptation that aims to learn a classifier with a slight target risk while labeled samples are only ...
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In this paper, we propose a Deep Hierarchical Optimal Transport method (DeepHOT) for unsupervised domain adaptation. The main idea is to use hierarchical ...
In DeepHOT framework, an image-level OT serves as the ground distance metric for the domain-level OT, leading to the hierarchical structural distance. Compared ...
ABSTRACT. Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from ...
Sep 30, 2022 · In this paper, we propose a novel approach for unsupervised domain adaptation that relates notions of optimal transport, learning probability measures, and ...
Inspired by this, we propose a novel method called Hierarchical Gradient Synchronization to model the synchronization relationship among the local distribution.
Missing: Distance | Show results with:Distance
Duration: 1:00
Posted: Jul 16, 2020
Posted: Jul 16, 2020
Missing: Distance | Show results with:Distance