74,365 research outputs found
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
Effects of system-bath entanglement on the performance of light-harvesting systems: A quantum heat engine perspective
We explore energy transfer in a generic three-level system, which is coupled
to three non-equilibrium baths. Built on the concept of quantum heat engine,
our three-level model describes non-equilibrium quantum processes including
light-harvesting energy transfer, nano-scale heat transfer, photo-induced
isomerization, and photovoltaics in double quantum-dots. In the context of
light-harvesting, the excitation energy is first pumped up by sunlight, then is
transferred via two excited states which are coupled to a phonon bath, and
finally decays to the ground state. The efficiency of this process is evaluated
by steady state analysis via a polaron-transformed master equation; thus a wide
range of the system-phonon coupling strength can be covered. We show that the
coupling with the phonon bath not only modifies the steady state, resulting in
population inversion, but also introduces a finite steady state coherence which
optimizes the energy transfer flux and efficiency. In the strong coupling
limit, the steady state coherence disappears and the efficiency approaches the
heat engine limit given by Scovil and Schultz-Dubois in Phys. Rew. Lett. 2, 262
(1959).Comment: 10 pages, 8 figures, all comments are welcom
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many
clustering and classification tasks. The classic ways to measure the errors
between the original and the reconstructed matrix are distance or
Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly
handled when we use these error measures. As a consequence, alternative
measures based on nonlinear kernels, such as correntropy, are proposed.
However, the current correntropy-based NMF only targets on the low-level
features without considering the intrinsic geometrical distribution of data. In
this paper, we propose a new NMF algorithm that preserves local invariance by
adding graph regularization into the process of max-correntropy-based matrix
factorization. Meanwhile, each feature can learn corresponding kernel from the
data. The experiment results of Caltech101 and Caltech256 show the benefits of
such combination against other NMF algorithms for the unsupervised image
clustering
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