Featured Publications
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
Liu X, Guo Z, Li S, Xing F, You J, Kuo C, Fakhri G, Woo J. Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate. 2021, 00: 10347-10356. DOI: 10.1109/iccv48922.2021.01020.Peer-Reviewed Original ResearchUnsupervised domain adaptationDomain adaptationLabel shiftUnsupervised domain adaptation methodsAdversarial unsupervised domain adaptationAlternating optimization schemeUDA methodsTarget domainTraining stageOptimization schemeTesting stageExperimental resultsDistribution w.AdversaryP(x|yP(y|xDomainSchemeClassificationMethodInferenceAdaptation
2023
Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model
Yoo C, Liu X, Xing F, Fakhri G, Woo J, Kang J. Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model. IEEE Transactions On Biomedical Engineering 2023, 70: 1252-1263. PMID: 36227815, DOI: 10.1109/tbme.2022.3214269.Peer-Reviewed Original ResearchConceptsProblem of domain shiftTarget networkConsumer-level devicesPre-trained modelsSleep staging performanceSevere noise levelsEfficient training approachAdversarial trainingAdversarial noiseDomain shiftAdversarial transformationsAuxiliary modelDL modelsInput perturbationsTraining modelArbitrary noiseTesting stageEnhanced robustnessNoise signalsTraining approachTest environmentExperimental resultsNoiseSleep stagesRobustness