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• W2890418037 abstract "Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors." @default.
• W2890418037 created "2018-09-27" @default.
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• W2890418037 date "2019-01-01" @default.
• W2890418037 modified "2023-10-03" @default.
• W2890418037 title "Low-resolution image categorization via heterogeneous domain adaptation" @default.
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• W2890418037 doi "https://doi.org/10.1016/j.knosys.2018.09.027" @default.
• W2890418037 hasPublicationYear "2019" @default.
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