Abstract
Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.
Highlights
We proposed a novel transfer learning method based on a convolutional neural network (CNN) [13] and use the squared-loss mutual information (SMI) [18] to measure the matching between the source and the target domain
(3) We develop an iterative algorithm to solve the minimization problem based on the alternating direction method of multipliers (ADMM) [27]
We proposed a novel CNN-based transfer learning method
Summary
We proposed a novel transfer learning method based on a convolutional neural network (CNN) [13] and use the squared-loss mutual information (SMI) [18] to measure the matching between the source and the target domain. (1) We build a novel transfer learning schema, which firstly represents the source and target domains by CNN models and constructs proxies for the target samples from the source domain. Is paper is organized as follows: in Section 2, we introduce the proposed transfer learning method based on CNN and proxy learning; in Section 3, we evaluate the proposed method experimentally over some benchmark data sets; in Section 4, we give the conclusion of this paper and some future works Paper Organization. is paper is organized as follows: in Section 2, we introduce the proposed transfer learning method based on CNN and proxy learning; in Section 3, we evaluate the proposed method experimentally over some benchmark data sets; in Section 4, we give the conclusion of this paper and some future works
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
