Cross-domain attribute representation based on convolutional neural network
Cross-domain attribute representation based on convolutional neural network
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DOI：
10.1007/978-3-319-95957-3_15
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Guohui Zhang;Gaoyuan Liang;Fang Su;Fanxin Qu;Jingyan Wang
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文献类型：
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作者：
Guohui Zhang;Gaoyuan Liang;Fang Su;Fanxin Qu;Jingyan Wang
在领域迁移学习问题中，我们从一些源领域和目标领域的数据中学习目标领域的预测模型，其中目标领域缺乏标签而源领域有足够的标签。除了数据实例之外，最近还对跨域共享数据的属性进行了探索，并被证明对利用不同域的信息非常有帮助。本文提出了一种基于实例和属性的领域迁移学习框架。我们提出通过共享卷积神经网络（CNN）嵌入不同领域的属性，通过跨领域的匹配学习一个独立于领域的CNN模型来表示不同领域共享的信息，以及一个特定于领域的CNN模型来表示每个领域的信息。使用三个CNN模型输出的串联来预测类标签。提出了一种基于梯度下降法的迭代算法来学习模型参数。在基准数据集上的实验表明了该模型的优越性。
In the problem of domain transfer learning, we learn a model for the prediction in a target domain from the data of both some source domains and the target domain, where the target domain is in lack of labels while the source domain has sufficient labels. Besides the instances of the data, recently the attributes of data shared across domains are also explored and proven to be very helpful to leverage the information of different domains. In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and attributes. We proposed to embed the attributes of different domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by different domains by matching across domains, and a domain-specific CNN model to represent the information of each domain. The concatenation of the three CNN model outputs is used to predict the class label. An iterative algorithm based on gradient descent method is developed to learn the parameters of the model. The experiments over benchmark datasets show the advantage of the proposed model.