3,003 research outputs found
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
We address the problem of instance-level facial attribute transfer without
paired training data, e.g. faithfully transferring the exact mustache from a
source face to a target face. This is a more challenging task than the
conventional semantic-level attribute transfer, which only preserves the
generic attribute style instead of instance-level traits. We propose the use of
geometry-aware flow, which serves as a well-suited representation for modeling
the transformation between instance-level facial attributes. Specifically, we
leverage the facial landmarks as the geometric guidance to learn the
differentiable flows automatically, despite of the large pose gap existed.
Geometry-aware flow is able to warp the source face attribute into the target
face context and generate a warp-and-blend result. To compensate for the
potential appearance gap between source and target faces, we propose a
hallucination sub-network that produces an appearance residual to further
refine the warp-and-blend result. Finally, a cycle-consistency framework
consisting of both attribute transfer module and attribute removal module is
designed, so that abundant unpaired face images can be used as training data.
Extensive evaluations validate the capability of our approach in transferring
instance-level facial attributes faithfully across large pose and appearance
gaps. Thanks to the flow representation, our approach can readily be applied to
generate realistic details on high-resolution images.Comment: To appear in AAAI 2019. Code and models are available at:
https://github.com/wdyin/GeoGA
Computation of Two-Dimensional Fourier Transforms for Noisy Band-Limited Signals
The computation of the two-dimensional Fourier transform by the sampling points creates an ill-posed problem. In this chapter, we will cover this problem for the band-limited signals in the noisy case. We will present a regularized algorithm based on the two-dimensional Shannon Sampling Theorem, the two-dimensional Fourier series, and the regularization method. First, we prove the convergence property of the regularized solution according to the maximum norm. Then an error estimation is given according to the L2-norm. The convergence property of the regularized Fourier series is given in theory, and some examples are given to compare the numerical results of the regularized Fourier series with the numerical results of the Fourier series
Transmit design for MIMO wiretap channel with a malicious jammer
In this paper, we consider the transmit design for multi-input multi-output
(MIMO) wiretap channel including a malicious jammer. We first transform the
system model into the traditional three-node wiretap channel by whitening the
interference at the legitimate user. Additionally, the eavesdropper channel
state information (ECSI) may be fully or statistically known, even unknown to
the transmitter. Hence, some strategies are proposed in terms of different
levels of ECSI available to the transmitter in our paper. For the case of
unknown ECSI, a target rate for the legitimate user is first specified. And
then an inverse water-filling algorithm is put forward to find the optimal
power allocation for each information symbol, with a stepwise search being used
to adjust the spatial dimension allocated to artificial noise (AN) such that
the target rate is achievable. As for the case of statistical ECSI, several
simulated channels are randomly generated according to the distribution of
ECSI. We show that the ergodic secrecy capacity can be approximated as the
average secrecy capacity of these simulated channels. Through maximizing this
average secrecy capacity, we can obtain a feasible power and spatial dimension
allocation scheme by using one dimension search. Finally, numerical results
reveal the effectiveness and computational efficiency of our algorithms.Comment: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring
Physical Layer Service Integration in 5G: Potentials and Challenges
High transmission rate and secure communication have been identified as the
key targets that need to be effectively addressed by fifth generation (5G)
wireless systems. In this context, the concept of physical-layer security
becomes attractive, as it can establish perfect security using only the
characteristics of wireless medium. Nonetheless, to further increase the
spectral efficiency, an emerging concept, termed physical-layer service
integration (PHY-SI), has been recognized as an effective means. Its basic idea
is to combine multiple coexisting services, i.e., multicast/broadcast service
and confidential service, into one integral service for one-time transmission
at the transmitter side. This article first provides a tutorial on typical
PHY-SI models. Furthermore, we propose some state-of-the-art solutions to
improve the overall performance of PHY-SI in certain important communication
scenarios. In particular, we highlight the extension of several concepts
borrowed from conventional single-service communications, such as artificial
noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These
techniques are shown to be effective in the design of reliable and robust
PHY-SI schemes. Finally, several potential research directions are identified
for future work.Comment: 12 pages, 7 figure
Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel
This paper considers an artificial noise (AN)-aided transmit design for
multi-user MIMO systems with integrated services. Specifically, two sorts of
service messages are combined and served simultaneously: one multicast message
intended for all receivers and one confidential message intended for only one
receiver and required to be perfectly secure from other unauthorized receivers.
Our interest lies in the joint design of input covariances of the multicast
message, confidential message and artificial noise (AN), such that the
achievable secrecy rate and multicast rate are simultaneously maximized. This
problem is identified as a secrecy rate region maximization (SRRM) problem in
the context of physical-layer service integration. Since this bi-objective
optimization problem is inherently complex to solve, we put forward two
different scalarization methods to convert it into a scalar optimization
problem. First, we propose to prefix the multicast rate as a constant, and
accordingly, the primal biobjective problem is converted into a secrecy rate
maximization (SRM) problem with quality of multicast service (QoMS) constraint.
By varying the constant, we can obtain different Pareto optimal points. The
resulting SRM problem can be iteratively solved via a provably convergent
difference-of-concave (DC) algorithm. In the second method, we aim to maximize
the weighted sum of the secrecy rate and the multicast rate. Through varying
the weighted vector, one can also obtain different Pareto optimal points. We
show that this weighted sum rate maximization (WSRM) problem can be recast into
a primal decomposable form, which is amenable to alternating optimization (AO).
Then we compare these two scalarization methods in terms of their overall
performance and computational complexity via theoretical analysis as well as
numerical simulation, based on which new insights can be drawn.Comment: 14 pages, 5 figure
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