Transcription of Learning Transferable Features with Deep Adaptation Networks
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Learning Transferable Features with deep Adaptation NetworksMingsheng Long Cao Wang I. Jordan School of Software, TNList Lab for Info. Sci. & Tech., Institute for Data Science, Tsinghua University, China Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USAA bstractRecent studies reveal that a deep neural networkcan learn Transferable Features which generalizewell to novel tasks for domain Adaptation . How-ever, as deep Features eventually transition fromgeneral to specific along the network , the featuretransferability drops significantly in higher layerswith increasing domain discrepancy. Hence, it isimportant to formally reduce the dataset bias andenhance the transferability in task-specific this paper, we propose a new deep AdaptationNetwork (DAN) architecture, which generalizesdeep convolutional neural network to the domainadaptation scenario.
deep networks, resulting in statistically unboundedrisk for target tasks (Mansour et al., 2009; Ben-David et al., 2010). Our work is primarily motivated by Yosinski et al. (2014), which comprehensively explores feature transferability of deep convolutional neural networks. The method focuses on a different scenario where the learning tasks are ...
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