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We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available.
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Minimum class confusion for versatile domain adaptation
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Progressive domain adaptation from a source pre-trained model
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Model adaptation: Unsupervised domain adaptation without source data
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Dual mixup regularized learning for adversarial domain adaptation
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Generative low-bitwidth data free quantization
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Bi-directional generation for unsupervised domain adaptation
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Collaborative unsupervised domain adaptation for medical image diagnosis
Yifan Zhang, Y. Wei, et al · 2020
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Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning
Yifan Zhang, Bryan Hooi, et al · 2021
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