Kernelized Domain Adaptation and Balanced Distribution Alignment for Image Classification

Document Type : Persian Original Article

Authors

1 Faculty of IT and Computer Engineering, Urmia University of Technology, Band, Western Azerbaijan Urmia, Iran

2 Faculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Transfer learning and domain adaptation are effective solutions for performance improvement of image classifiers where the source domain (training set) and target domain (test set) have substantial probability distribution differences. In fact, collecting input data in various conditions (such as lighting or temperature), different equipment with variable characteristics (such as number of input ports or resolution quality) and different views (such as dimensions or environment) results domain shift problem. Semi-supervised domain adaptation is a leading solution for domain shift problem, where the source domain and a small part of target domain are labeled. In this paper, we propose KErnelized Domain Adaptation and balanced distributions alignment (KEDA) to adapt the source and target domains in a semi-supervised manner. KEDA preserves the topology of domains via creating a Laplacian matrix and similarity and dissimilarity views. Moreover, KEDA adapts the regularized conditional and marginal distributions across domains. Ultimately, the sum of these solutions leads to a good classification function for labeling the unlabeled images. The proposed method is compared with the state-of-the-art methods of domain adaptation on Office-Caltech-10, Digits, Pie, and Coil datasets where results show considerable performance improvement of our proposed method.

Keywords


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