5,150 research outputs found
Importance sampling the union of rare events with an application to power systems analysis
We consider importance sampling to estimate the probability of a union
of rare events defined by a random variable . 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 conditionally on that event
happening and it constructs an unbiased estimate of 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 , it has a
variance no larger than where is the union
bound. It also has a coefficient of variation no larger than
regardless of the overlap pattern among the
events. Our motivating problem comes from power system reliability, where the
phase differences between connected nodes have a joint Gaussian distribution
and the rare events arise from unacceptably large phase differences. In the
grid reliability problems even some events defined by constraints in
dimensions, with probability below , are estimated with a
coefficient of variation of about with only sample
values
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
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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