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Analysis of Representations for Domain Adaptation - NeurIPS We presented an analysis of representations for domain adaptation It is reasonable to think that a good representation is the key to effective domain adaptation, and our theory backs up that intuition
Analysis of Representations for Domain Adaptation - MIT Press "Analysis of Representations for Domain Adaptation", Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, Bernhard Schölkopf, John Platt, Thomas Hofmann
Analysis of Representations for Domain Adaptation | 利醬の休憩房 A good feature representation is crucial for the success of domain adaptation The paper formalizes this intuition with a generalization bound for domain adaptation, highlighting the trade-offs in designing feature representations
Analysis of Representations for Domain Adaptation This paper presents a two-stage approach to domain adaptation, where at the first generalization stage, the author looks for a set of features generalizable across domains, and at the second adaptation stage, heuristics to pick up useful features specific to the target domain
On Learning Invariant Representations for Domain Adaptation Abstract Due to the ability of deep neural nets to learn rich representations, recent advances in unsuper-vised domain adaptation have focused on learning domain-invariant features that achieve a small er-ror on the source domain
RDAM: Domain adaptation under small and class-imbalanced samples Domain Adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain by aligning their feature distributions Although notable advancements have been achieved, domain adaptation remains challenging for scenarios involving small sample size and class-imbalanced datasets, where limited and imbalanced data hinder effective domain alignment To address this issue