WebWe propose a Pareto Domain Adaptation (ParetoDA) approach to control the overall optimization direction, aiming to cooperatively optimize all training objectives. Specifically, to reach a desirable solution on the target domain, we design a surrogate loss mimicking target classification. WebFeb 23, 2024 · Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source domain data that share a common label space but follow a different distribution. Most of the recent methods take the approach of explicitly aligning feature distributions between the two domains. Differently, motivated …
【NeurIPS 2024】帕累托域适应 - 知乎 - 知乎专栏
WebOct 7, 2024 · A Brief Review of Domain Adaptation. Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test ... WebWe propose a Pareto Domain Adaptation (ParetoDA) approach to control the overall optimization direction, aiming to cooperatively optimize all training objectives. Specifically, to reach a desirable solution on the target domain, we design a surrogate loss mimicking target classification. To improve target-prediction accuracy to support the ... bixby coffee bentwood recliner
Class-Imbalanced Domain Adaptation: An Empirical Odyssey
WebApr 15, 2024 · Based on the mathematical concept of multi-objective Pareto optimization, its adaptation, implementation and application in the context of Smart Cities are presented in detail. WebDomain adaptation is one part of transfer learning where transfer of knowledge occurs between two domains, source and target. Domain adaptation approaches differ from … WebDomain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve this, DA methods include a source classification objective LS to extract the source knowledge and a domain alignment objective LD to diminish the domain shift ... bixby coffee