16,347 research outputs found
Constrained Deep Transfer Feature Learning and its Applications
Feature learning with deep models has achieved impressive results for both
data representation and classification for various vision tasks. Deep feature
learning, however, typically requires a large amount of training data, which
may not be feasible for some application domains. Transfer learning can be one
of the approaches to alleviate this problem by transferring data from data-rich
source domain to data-scarce target domain. Existing transfer learning methods
typically perform one-shot transfer learning and often ignore the specific
properties that the transferred data must satisfy. To address these issues, we
introduce a constrained deep transfer feature learning method to perform
simultaneous transfer learning and feature learning by performing transfer
learning in a progressively improving feature space iteratively in order to
better narrow the gap between the target domain and the source domain for
effective transfer of the data from the source domain to target domain.
Furthermore, we propose to exploit the target domain knowledge and incorporate
such prior knowledge as a constraint during transfer learning to ensure that
the transferred data satisfies certain properties of the target domain. To
demonstrate the effectiveness of the proposed constrained deep transfer feature
learning method, we apply it to thermal feature learning for eye detection by
transferring from the visible domain. We also applied the proposed method for
cross-view facial expression recognition as a second application. The
experimental results demonstrate the effectiveness of the proposed method for
both applications.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Generation of Multi-Color Attosecond X-Ray Radiation Through Modulation Compression
In this paper, we propose a scheme to generate tunable multi-color attosecond
coherent X-ray radiation for future light source applications. This scheme uses
an energy chirped electron beam, a laser modulators, a laser chirper and two
bunch compressors to generate a multi-spike prebunched kilo-Ampere current
electron beam from a few tens Ampere electron beam out of a linac. Such an
electron beam transports through a series of undulator radiators and bunch
compressors to generate multi-color coherent X-ray radiation. As an
illustration, we present an example to generate two attosecond pulses with
nm and nm coherent X-ray radiation wavelength and more than MW
peak power using a Ampere nm laser seeded electron beam
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Cascade regression framework has been shown to be effective for facial
landmark detection. It starts from an initial face shape and gradually predicts
the face shape update from the local appearance features to generate the facial
landmark locations in the next iteration until convergence. In this paper, we
improve upon the cascade regression framework and propose the Constrained Joint
Cascade Regression Framework (CJCRF) for simultaneous facial action unit
recognition and facial landmark detection, which are two related face analysis
tasks, but are seldomly exploited together. In particular, we first learn the
relationships among facial action units and face shapes as a constraint. Then,
in the proposed constrained joint cascade regression framework, with the help
from the constraint, we iteratively update the facial landmark locations and
the action unit activation probabilities until convergence. Experimental
results demonstrate that the intertwined relationships of facial action units
and face shapes boost the performances of both facial action unit recognition
and facial landmark detection. The experimental results also demonstrate the
effectiveness of the proposed method comparing to the state-of-the-art works.Comment: International Conference on Computer Vision and Pattern Recognition,
201
One loop partition function in AdS_3/CFT_2
The 1-loop partition function of the handle-body solutions in the AdS
gravity have been derived some years ago using the heat-kernel and the method
of images. In the semiclassical limit, such partition function should
correspond to the order part in the partition function of dual
conformal field theory on the boundary Riemann surface. The higher genus
partition function could be computed by the multi-point functions in the
Riemann sphere via sewing prescription. In the large central charge limit, to
the leading order of , the multi-point function is further simplified to be
a summation over the product of two-point functions, which may form links. Each
link is in one-to-one correspondence with the conjugacy class of the Schottky
group of the Riemann surface. Moreover, the value of a link is determined by
the eigenvalue of the element in the conjugate class. This allows us to
reproduce exactly the gravitational 1-loop partition function. The proof can be
generalized to the higher spin gravity and its dual CFT.Comment: 30 pages, 8 figures; typos corrected, more clarifications, references
and acknowledgements adde
R\'enyi Entropy of Free Compact Boson on Torus
In this paper, we reconsider the single interval R\'enyi entropy of a free
compact scalar on a torus. In this case, the contribution to the entropy could
be decomposed into classical part and quantum part. The classical part includes
the contribution from all the saddle points, while the quantum part is
universal. After considering a different monodromy condition from the one in
the literature, we re-evaluate the classical part of the R\'enyi entropy.
Moreover, we expand the entropy in the low temperature limit and find the
leading thermal correction term which is consistent with the universal behavior
suggested in arXiv:1403.0578 [hep-th]. Furthermore we investigate the large
interval behavior of the entanglement entropy and show that the universal
relation between the entanglement entropy and thermal entropy holds in this
case.Comment: 16 pages. Improved arguments, added referenc
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