A representation learning approach for multi-source domain adaptation achieves identifiability by partitioning the label's Markov blanket into parents, children, and spouses.
Moment matching for multi-source domain adaptation
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
MemFlow uses forward-only memorization via randomly connected neurons and spiking signals on a frozen backbone for unsupervised domain adaptation, claiming up to 10% gains at under 1% of traditional compute costs.
FOCoOp uses global, local, and OOD prompts with bi-level distributionally robust optimization and semi-unbalanced optimal transport to improve OOD robustness in federated prompt learning.
citing papers explorer
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A General Representation-Based Approach to Multi-Source Domain Adaptation
A representation learning approach for multi-source domain adaptation achieves identifiability by partitioning the label's Markov blanket into parents, children, and spouses.
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MemFlow: A Lightweight Forward Memorizing Framework for Quick Domain Adaptive Feature Mapping
MemFlow uses forward-only memorization via randomly connected neurons and spiking signals on a frozen backbone for unsupervised domain adaptation, claiming up to 10% gains at under 1% of traditional compute costs.
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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models
FOCoOp uses global, local, and OOD prompts with bi-level distributionally robust optimization and semi-unbalanced optimal transport to improve OOD robustness in federated prompt learning.