41,201 research outputs found

    Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

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    Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

    Deep Clustering: A Comprehensive Survey

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    Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

    Marshallian Sources of Growth and Interdependent Location of Swedish Firms and Households

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    This thesis consists of three papers that examine Marshallian sources of growth and interdependent location of Swedish firms and households. Paper [I] examines the impact of static and dynamic knowledge externalities and their impact on Swedish market operating firms growth pattern between 1997 and 2005. The three types of externalities are: (i) Marshall-Arrow-Romer (MAR), (ii) Jacobs, and (iii) Porter. My empirical findings for the 40 industries can briefly be summarized in the following points: (i) static MAR, Jacobs and/or Porter externalities are present in all but nine industries; (ii) except for five cases all industries are exposed to one or more of the MAR, Jacobs and/or Porter type of dynamic externalities; (iii) contrary to previous studies but in line with theoretical predictions, we do find positive and significant effects for static as well as dynamic Jacobs externalities. Paper [II] focuses on the presence of agglomeration economies in the form of labor pooling and educational matching and their impact on economic growth in Swedish manufacturing and service industries from 1997 to 2005. To accom- plish this I employ a translog production function that enables me to decompose the total agglomeration elasticities into returns that accrue to: direct agglom- eration effects, an indirect effect of agglomeration at given input levels, a cross agglomeration effect of matching on labor pooling and vice versa. Household services is the single industry where both the labor pooling and matching hy- pothesis is supported by our data. Publishing is the sole instance of better input usage due to matching consistent with the theoretical claim. Paper [III] studies the interdependent location choices of households and firms expressed as population and employment in Swedish municipalities. Using a model of the Carlino-Mills type to investigate the impact of various location attributes such as differences in public revenue and spending patterns, accessi- bility to jobs and potential workforce, quality of the labor pool, concentration of commercial, private and public services. The findings suggest that fiscal factors significantly alters the impact of housing and accessibility attributes compared to exiting studies on Swedish data. Another finding, in line with previous stud- ies, indicate that there is a significant degree of inertia in household and firm location choices.Information and knowledge spillover; MAR; Jacobs and Porter externalities; labor pooling; interdependent location choice; panel data
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