Unsupervised domain adaptation via importance sampling

• Main Authors: XU, Xuemiao, HE, Hai, ZHANG, Huaidong, XU, Yangyang, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Feature extraction; Entropy; Tuning; Estimation; Monte Carlo methods; Adaptation models; Noise measurement; Domain adaptation; deep learning; distribution sampling; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7884
• Institution: Singapore Management University

Unsupervised domain adaptation aims to generalize a model from the label-rich source domain to the unlabeled target domain. Existing works mainly focus on aligning the global distribution statistics between source and target domains. However, they neglect distractions from the unexpected noisy sampl...