PARA
发表于 2025-3-30 10:28:18
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gruelling
发表于 2025-3-30 16:06:09
https://doi.org/10.1007/978-1-349-19749-1active learning, or DAST-AL framework, that looks ahead the effect of ISDA in the selection of unlabeled samples. Specifically, DAST-AL exploits expected partial model change maximization (EPMCM) to consider selected samples’ potential contribution of the diversity to the labeled set by leveraging t
dictator
发表于 2025-3-30 18:13:41
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使虚弱
发表于 2025-3-30 22:04:29
Investment and Technology Choice us to consider the classes (which are already labeled) as the varying environments (The word “environments” [.] denotes the subsets of training data built by some criteria. In this paper, we take a class as an environment—our key idea.) to resolve context bias (without context labels). We implement
过分自信
发表于 2025-3-31 04:47:08
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染色体
发表于 2025-3-31 07:26:27
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Estimable
发表于 2025-3-31 10:14:40
Investment and Technology Choicen generative models for the target task. We demonstrate the effectiveness of RealPatch on three benchmark datasets, CelebA, Waterbirds and a subset of iWildCam, showing improvements in worst-case subgroup performance and in subgroup performance gap in binary classification. Furthermore, we conduct e
哎呦
发表于 2025-3-31 15:09:19
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Substitution
发表于 2025-3-31 20:30:10
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