Objective

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Many image recognition methods demonstrate good performance when applied to training and test data extracted from the same distribution. However

these methods are unsuitable in practical scenarios and result in low performance. Using domain adaptive methods is an effective approach for solving such problem. Domain adaptation aims to solve various problems

such as when data are from two related domains but with different distributions. In practical applications

labeling data takes substantial manual labor. Thus

unsupervised learning has become a clear trend in image recognition. Transfer learning can extract knowledge from the labeled data in the source domain and transfer it to the unlabeled target domain.

Method

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We propose a joint balanced adaptive method based on attention transfer mechanism

which transfers feature representations extracted from the labeled datasets in the source domain to the unlabeled datasets in the target domain. Specifically

we first transfer the labeled source-domain space category information to the unlabeled target domain via attention transfer mechanism. Neural networks reflect the basic characteristics of the human brain

and attention is precisely an important part of the human visual experience and closely related to perception. Artificial attention mechanism started to be developed as artificial neural network has become increasingly popular in various fields

such as computer vision and pattern recognition. Allowing a system to learn attending objects and understand the mechanism behind neural networks has become a research tool. Attention information can be used to improve image recognition accuracy significantly by defining the attention of convolutional neural networks (CNNs). In this study

attention can be seen as a set of spatial mappings that encode the spatial regions highly concerned with the network input to determine its possible output. Second

we introduce the prior distribution of the network parameters on the basis of the target dataset and endow the layer with the capability of automatically learning the alignment degree that should be pursued at different levels of the network. We expect to explore abundant source-domain attributes through cross-domain learning and capture substantial complex cross-domain knowledge by embedding cross-dataset information for minimizing the original function loss for the learning tasks in two domains as much as possible. Machine learning is an alternative approach for recognizing the refined features after preprocessing raw data into features on the basis of prior knowledge of humans. Machine learning experts have spent most of their time designing features in the past few years because recognition results depend on the quality of features. Recent breakthrough in object recognition has been mainly achieved by approaches based on deep CNN due to its more powerful feature extraction and image representation capabilities than manually defined features

such as HOG and SIFT. The higher the network layers are

the more specific the characteristics are for the target categorization tasks. Meanwhile

the features on successive layers interact with each other in a complex and fragile way. Accordingly

the neurons between neighboring layers co-adapt during training. Therefore

the mobility of features and classifiers decreases as the cross-domain difference increases. Finally

we describe the input distribution of the domain-specific adaptive alignment layer by introducing cross-domain biases

thereby quantitatively indicating the inter-domain adaptation degree that each layer learns. Meanwhile

we adaptively change the weight of each category in the dataset. Although deep CNN is a unified training and prediction framework that combines multi-level feature extractors and recognizers

end-to-end processing is particularly important. The design concept for our model fully utilizes the capability of CNN to perform end-to-end processing.

Result

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The average recognition accuracies of the method in datasets Office-31 and Office-Caltech are 77.6% and 90.7%

respectively. Thus

this method significantly outperforms traditional methods based on handcrafted feature and is also comparable with state-of-the-art methods. Although not all single transfer tasks achieve optimal results

the average recognition accuracy of the six transfer tasks is improved compared with the current mainstream methods.

Conclusion

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Transferring image features extracted from labeled data in the source domain to the unlabeled target domain effectively solves data problems from two domains that are related but differently distributed. The method fully utilizes the spatial location information of the labeled data in the source domain through attention transfer mechanism and uses the deep CNN to learn the alignment degree of the features between domains automatically. Learning ability largely depends on the degree of inter-domain correlation

which is a major limitation for transfer learning. In addition

knowledge transition is apparently ineffective if no similarity exists between the domains. Thus

we fully consider the feature correlation in the dataset between source and target domains and adaptively change the weight of each category in the dataset. Our method can not only effectively obtain high recognition accuracy but also automatically learn the degree of feature alignment between domains. This method also verifies that the inter-domain feature transfer can improve network optimization effect.