Feature analysis of marginalized stacked denoising autoenconder for unsupervised domain adaptation

• Main Authors: Wei, Pengfei, Ke, Yiping, Goh, Chi Keong
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Deep Feature Learning and Feature Analysis; Marginalized Denoising Autoencoder
• Online Access: https://hdl.handle.net/10356/151969
• Institution: Nanyang Technological University

Marginalized stacked denoising autoencoder (mSDA), has recently emerged with demonstrated effectiveness in domain adaptation. In this paper, we investigate the rationale for why mSDA benefits domain adaptation tasks from the perspective of adaptive regularization. Our investigations focus on two typ...