28,433 research outputs found
Enhancing Perceptual Attributes with Bayesian Style Generation
Deep learning has brought an unprecedented progress in computer vision and
significant advances have been made in predicting subjective properties
inherent to visual data (e.g., memorability, aesthetic quality, evoked
emotions, etc.). Recently, some research works have even proposed deep learning
approaches to modify images such as to appropriately alter these properties.
Following this research line, this paper introduces a novel deep learning
framework for synthesizing images in order to enhance a predefined perceptual
attribute. Our approach takes as input a natural image and exploits recent
models for deep style transfer and generative adversarial networks to change
its style in order to modify a specific high-level attribute. Differently from
previous works focusing on enhancing a specific property of a visual content,
we propose a general framework and demonstrate its effectiveness in two use
cases, i.e. increasing image memorability and generating scary pictures. We
evaluate the proposed approach on publicly available benchmarks, demonstrating
its advantages over state of the art methods.Comment: ACCV-201
Precise Formulation of Neutrino Oscillation in the Earth
We give a perturbation theory of neutrino oscillation in the Earth. The
perturbation theory is valid for neutrinos with energy E \gsim 0.5 GeV. It is
formulated using trajectory dependent average potential. Non-adiabatic
contributions are included as the first order effects in the perturbation
theory. We analyze neutrino oscillation with standard matter effect and with
non-standard matter effect. In a three flavor analysis we show that the
perturbation theory gives a precise description of neutrino conversion in the
Earth. Effect of the Earth matter is substantially simplified in this
formulation.Comment: References added, 21 pages, 10 figures, version to appear in PR
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient
algorithm for learning finite-state controllers (FSCs) in large decentralized
POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often
converge to maxima that are far from optimal. This paper considers a
variable-size FSC to represent the local policy of each agent. These
variable-size FSCs are constructed using a stick-breaking prior, leading to a
new framework called \emph{decentralized stick-breaking policy representation}
(Dec-SBPR). This approach learns the controller parameters with a variational
Bayesian algorithm without having to assume that the Dec-POMDP model is
available. The performance of Dec-SBPR is demonstrated on several benchmark
problems, showing that the algorithm scales to large problems while
outperforming other state-of-the-art methods
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