12,276 research outputs found
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2017
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising from the
primary collision of interest (leading vertex) by radiation from soft
collisions (pileup). We develop a new technique for removing this contamination
using machine learning and convolutional neural networks. The network takes as
input the energy distribution of charged leading vertex particles, charged
pileup particles, and all neutral particles and outputs the energy distribution
of particles coming from leading vertex alone. The PUMML algorithm performs
remarkably well at eliminating pileup distortion on a wide range of simple and
complex jet observables. We test the robustness of the algorithm in a number of
ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio
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