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Deep models with multiple layers have demonstrated their potential in learning abstract and invariant features for better representation and classification of remote sensing images. Moreover, metric learning (ML) is usually introduced into the deep models to further increase the discrimination of deep representations. However, the usual deep ML methods treat the training samples in each training batch...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends...
Facial attractiveness computation is a challenging task because of the lack of labeled data and discriminative features. In this paper, an end-to-end label distribution learning (LDL) framework with deep convolutional neural network (CNN) and geometric features is proposed to meet these two challenges. Different from the previous work, we recast this task as an LDL problem. Compared with the single...
In MANETs, link failures are caused frequently due to node's mobility and use of unreliable wireless channels for data transmission. Multipath routing strategy can cope with the problem of the traffic overloads while balancing the network resource consumption. This article proposes an improved approach named DNDR (Dual Node-Disjoint Paths Routing) to enhance the network reliability and robustness...
Task allocation strategy has a great impact on the performance of the workflow management system in workflow scheduling. Most task allocation algorithms focus on the resource status only. Few algorithms consider the change of efficiency while performer working with different people. To achieve this, this paper presents a concept of Social Context Impact Factor (SCIF) and implements several task allocation...
In this paper, we propose an improved soft-output M-algorithm (ISOMA) and use it to reduce the computational complexity of differential encoded LDPC (DE-LDPC) coded systems with multiple-symbol differential detection (MSDD). The proposed ISOMA combines the features of iterative tree search detection based on M-algorithm (ITS-MA) and soft-output M-algorithm (SOMA) approaches, which can guarantee that...
Automatic Term Recognition (ATR) is an important task for Knowledge Acquisition, which aims at acquiring formalized words which are not recorded in time in the glossary. In recent years, several statistical methods has proved to be effective, and emerging methods such as C-value, NC-Value, TermExtractor has shown great advantages on this task. However, few works have been done on the Metric mixing...
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