Feature analysis of marginalized stacked denoising autoenconder for unsupervised domain adaptation

Feature analysis of marginalized stacked denoising autoenconder for unsupervised domain adaptation

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...

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書目詳細資料
Main Authors:	Wei, Pengfei, Ke, Yiping, Goh, Chi Keong
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2021
主題:	Engineering::Computer science and engineering Deep Feature Learning and Feature Analysis Marginalized Denoising Autoencoder
在線閱讀:	https://hdl.handle.net/10356/151969
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

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