Semi-supervised domain adaptation via convolutional neural network
文献类型：会议论文
| 作者 | Liu, Pengcheng ; Feng, Youji; Cheng, Cheng; Shao, Xiaohu; Zhou, Xiangdong
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| 出版日期 | 2018 |
| 会议日期 | September 17, 2017 - September 20, 2017 |
| 会议地点 | Beijing, China |
| DOI | 10.1109/ICIP.2017.8296801 |
| 页码 | 2841-2845 |
| 英文摘要 | Semi-supervised visual domain adaptation is devoted to adapting a model learned in source domain to target domain where there are only a few labeled samples. In this paper, we propose a semi-supervised cross-domain image recognition method which unifies the feature learning and recognition model training into a convolutional neural network framework. Based on a few labeled samples and massive unlabeled samples in the source and target domains, we specially design three branches for class label, domain label and similarity label prediction which simultaneously optimizes the network to generate image features that are domain invariance and inter-class discriminative. Experimental results demonstrate that our method is effective for learning robust cross-domain image recognition model, and achieves the state-of-the-art performance on the widely used visual domain adaptation benchmark. © 2017 IEEE. |
| 会议录 | 24th IEEE International Conference on Image Processing, ICIP 2017
 |
| 语种 | 英语 |
| ISSN号 | 15224880 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/7955]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 作者单位 | Intelligent Media Technique Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Liu, Pengcheng,Feng, Youji,Cheng, Cheng,et al. Semi-supervised domain adaptation via convolutional neural network[C]. 见:. Beijing, China. September 17, 2017 - September 20, 2017. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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