KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Semi-supervised domain adaptation via convolutional neural network | |
Liu, Pengcheng ; Feng, Youji; Cheng, Cheng; Shao, Xiaohu; Zhou, Xiangdong
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| 2018 | |
| 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. |
| 语种 | 英语 |
| DOI | 10.1109/ICIP.2017.8296801 |
| 会议(录)名称 | 24th IEEE International Conference on Image Processing, ICIP 2017
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| 页码 | 2841-2845 |
| 收录类别 | EI |
| 会议地点 | Beijing, China |
| 会议日期 | September 17, 2017 - September 20, 2017 |
