Web: http://arxiv.org/abs/2201.11613

Jan. 28, 2022, 2:11 a.m. | David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Ralf Mikut, Albrecht Schmidt, Ozan Özdenizci

cs.LG updates on arXiv.org arxiv.org

Deep learning based electroencephalography (EEG) signal processing methods
are known to suffer from poor test-time generalization due to the changes in
data distribution. This becomes a more challenging problem when
privacy-preserving representation learning is of interest such as in clinical
settings. To that end, we propose a multi-source learning architecture where we
extract domain-invariant representations from dataset-specific private
encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain
alignment approach to impose domain-invariance for encoded representations,
which outperforms state-of-the-art approaches in …

arxiv learning representation representation learning

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