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Found 3 papers, 1 papers with code
On Target Shift in Adversarial Domain Adaptation
In this work, we propose a method called Domain Adversarial nets for Target Shift (DATS) to address label shift while learning a domain invariant representation.
Extracting Relationships by Multi-Domain Matching
This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial.
Targeting EEG/LFP Synchrony with Neural Nets
We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e. g., conventional deep learning methods).
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