Deep Adversarial Learning and Domain Adaptation
碩士 === 國立交通大學 === 電機工程學系 === 105 === Deep learning has been rapidly developing from different aspects of theories and applications where a large amount of labeled data are available for supervised training. However, in practice, it is time-consuming to collect a large set of labeled data. In real wo...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/3848u8 |