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cs.LG2023
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
Adam Breitholtz, Fredrik D. Johansson
Understanding generalization is crucial to confidently engineer and deploy machine learning models, especially when deployment implies a shift in the data domain. For such domain a…
cs.LG2023★ 1 cited
Unsupervised domain adaptation by learning using privileged information
Adam Breitholtz, Anton Matsson, Fredrik D. Johansson
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in hig…