26,462 research outputs found
Photorealistic Style Transfer with Screened Poisson Equation
Recent work has shown impressive success in transferring painterly style to
images. These approaches, however, fall short of photorealistic style transfer.
Even when both the input and reference images are photographs, the output still
exhibits distortions reminiscent of a painting. In this paper we propose an
approach that takes as input a stylized image and makes it more photorealistic.
It relies on the Screened Poisson Equation, maintaining the fidelity of the
stylized image while constraining the gradients to those of the original input
image. Our method is fast, simple, fully automatic and shows positive progress
in making a stylized image photorealistic. Our results exhibit finer details
and are less prone to artifacts than the state-of-the-art.Comment: presented in BMVC 201
Geometry of the faithfulness assumption in causal inference
Many algorithms for inferring causality rely heavily on the faithfulness
assumption. The main justification for imposing this assumption is that the set
of unfaithful distributions has Lebesgue measure zero, since it can be seen as
a collection of hypersurfaces in a hypercube. However, due to sampling error
the faithfulness condition alone is not sufficient for statistical estimation,
and strong-faithfulness has been proposed and assumed to achieve uniform or
high-dimensional consistency. In contrast to the plain faithfulness assumption,
the set of distributions that is not strong-faithful has nonzero Lebesgue
measure and in fact, can be surprisingly large as we show in this paper. We
study the strong-faithfulness condition from a geometric and combinatorial
point of view and give upper and lower bounds on the Lebesgue measure of
strong-faithful distributions for various classes of directed acyclic graphs.
Our results imply fundamental limitations for the PC-algorithm and potentially
also for other algorithms based on partial correlation testing in the Gaussian
case.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1080 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Generative Face Completion
In this paper, we propose an effective face completion algorithm using a deep
generative model. Different from well-studied background completion, the face
completion task is more challenging as it often requires to generate
semantically new pixels for the missing key components (e.g., eyes and mouths)
that contain large appearance variations. Unlike existing nonparametric
algorithms that search for patches to synthesize, our algorithm directly
generates contents for missing regions based on a neural network. The model is
trained with a combination of a reconstruction loss, two adversarial losses and
a semantic parsing loss, which ensures pixel faithfulness and local-global
contents consistency. With extensive experimental results, we demonstrate
qualitatively and quantitatively that our model is able to deal with a large
area of missing pixels in arbitrary shapes and generate realistic face
completion results.Comment: Accepted by CVPR 201
Degraded acceptability and markedness in syntax, and the stochastic interpretation of optimality theory
The argument that I tried to elaborate on in this paper is that the conceptual problem behind the traditional competence/performance distinction does not go away, even if we abandon its original Chomskyan formulation. It returns as the question about the relation between the model of the grammar and the results of empirical investigations â the question of empirical verification The theoretical concept of markedness is argued to be an ideal correlate of gradience. Optimality Theory, being based on markedness, is a promising framework for the task of bridging the gap between model and empirical world. However, this task not only requires a model of grammar, but also a theory of the methods that are chosen in empirical investigations and how their results are interpreted, and a theory of how to derive predictions for these particular empirical investigations from the model. Stochastic Optimality Theory is one possible formulation of a proposal that derives empirical predictions from an OT model. However, I hope to have shown that it is not enough to take frequency distributions and relative acceptabilities at face value, and simply construe some Stochastic OT model that fits the facts. These facts first of all need to be interpreted, and those factors that the grammar has to account for must be sorted out from those about which grammar should have nothing to say. This task, to my mind, is more complicated than the picture that a simplistic application of (not only) Stochastic OT might draw
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