5,150 research outputs found

    Importance sampling the union of rare events with an application to power systems analysis

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    We consider importance sampling to estimate the probability μ\mu of a union of JJ rare events HjH_j defined by a random variable x\boldsymbol{x}. The sampler we study has been used in spatial statistics, genomics and combinatorics going back at least to Karp and Luby (1983). It works by sampling one event at random, then sampling x\boldsymbol{x} conditionally on that event happening and it constructs an unbiased estimate of μ\mu by multiplying an inverse moment of the number of occuring events by the union bound. We prove some variance bounds for this sampler. For a sample size of nn, it has a variance no larger than μ(μˉ−μ)/n\mu(\bar\mu-\mu)/n where μˉ\bar\mu is the union bound. It also has a coefficient of variation no larger than (J+J−1−2)/(4n)\sqrt{(J+J^{-1}-2)/(4n)} regardless of the overlap pattern among the JJ events. Our motivating problem comes from power system reliability, where the phase differences between connected nodes have a joint Gaussian distribution and the JJ rare events arise from unacceptably large phase differences. In the grid reliability problems even some events defined by 57725772 constraints in 326326 dimensions, with probability below 10−2210^{-22}, are estimated with a coefficient of variation of about 0.00240.0024 with only n=10,000n=10{,}000 sample values

    CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

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    The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.Comment: CVPR 202
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