Oct 17, 2021 · Conventional Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain ...
Imbalanced Source-free Domain Adaptation Framework. ISFDA Framework. Prerequisites. Datasets: Please download the VisDA-C dataset, Office-Home dataset and ...
May 22, 2023 · Abstract:Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a well-trained source model to an unlabeled target domain without ...
Imbalanced Source-free Domain Adaptation - Semantic Scholar
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Imbalanced Source-free Domain Adaptation is presented, which first train a uniformed model from the source domain, and then proposes secondary label ...
Oct 24, 2021 · Conventional Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unla- beled target domain ...
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We estimate class distribution by summing over model prediction probabilities to more flexibly tackle the uncertainty in an imbalanced dataset.
Oct 17, 2021 · Conventional Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain ...
Jan 16, 2024 · Our method shows superior adaptation performance in SFDA scenarios, including 2D image and 3D point cloud datasets and a highly imbalanced ...
We perform a thorough evaluation of the proposed universal source-free domain adaptation framework against prior state-.
Jan 7, 2024 · SFUDA emerges as a practical and novel task that enables a pre-trained model to adapt to a new unlabeled domain without access to the original training data.