小官
发表于 2025-3-23 10:15:52
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放牧
发表于 2025-3-23 16:05:59
Book 2024based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents act
捐助
发表于 2025-3-23 18:08:44
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Mettle
发表于 2025-3-24 02:05:24
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Peak-Bone-Mass
发表于 2025-3-24 03:29:59
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Jocose
发表于 2025-3-24 09:17:11
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污点
发表于 2025-3-24 12:11:23
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不自然
发表于 2025-3-24 16:21:02
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antenna
发表于 2025-3-24 19:36:15
Active Learning for Unsupervised Domain Adaptation,roduces two novel techniques to address key limitations of existing active domain adaptation (ADA) methods: estimating target representativeness without source data access and probabilistic uncertainty estimation. First, an energy-based criterion is proposed for selecting representative target sampl
Genistein
发表于 2025-3-25 03:13:12
Continual Test-Time Unsupervised Domain Adaptation,main data during inference with a continuously changing data distribution. Previous methods have been found to lack noise robustness, leading to a significant increase in errors under strong noise. In this chapter, we address the noise robustness problem in continual TTA by offering three effective