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Found 6 papers, 1 papers with code
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
Based on this novel algorithm, we propose two new strategies for MSDA: GMM-WBT and GMM-DaDiL.
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions.
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching.
Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of Chemical Processes
Nonetheless, these models are sensible to changes in the data distributions, which may be caused by changes in the monitored process, such as changes in the mode of operation.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.
Recent Advances in Optimal Transport for Machine Learning
Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions.
