5,924 research outputs found

    High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

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    We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example

    A Meta-Analytic Review of More than a Decade of Research on General Computer Self-Efficacy: Research in Progress

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    In their seminal work, Compeau and Higgins (1995) provided the IS research community with a measure of computer selfefficacy (CSE) based on Bandura’s (1986) Social Cognitive Theory. The use of this CSE measure has since flourished within various academic literatures. Recent research interest (Marakas, Johnson, & Clay, 2007; Thatcher, Zimmer, Gundlach et al., 2008), however, challenges the continued application and analysis of Compeau and Higgins’ (1995) measure despite its widespread adoption. This paper presents the results of a meta-analysis of general CSE provided through the foundation of technology adoption research. The results should create future dialogue regarding general CSE and its application. We show evidence of moderate associations (r = |0.32| to |0.59|) of general CSE with several technology adoption research constructs. Guidance is offered for future moderator analyses, which may likely provide empirical evidence for either the support or refutation of current research claims in regard to general CSE
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