3,784 research outputs found
Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
A crucial aspect of mass-mapping, via weak lensing, is quantification of the
uncertainty introduced during the reconstruction process. Properly accounting
for these errors has been largely ignored to date. We present results from a
new method that reconstructs maximum a posteriori (MAP) convergence maps by
formulating an unconstrained Bayesian inference problem with Laplace-type
-norm sparsity-promoting priors, which we solve via convex
optimization. Approaching mass-mapping in this manner allows us to exploit
recent developments in probability concentration theory to infer theoretically
conservative uncertainties for our MAP reconstructions, without relying on
assumptions of Gaussianity. For the first time these methods allow us to
perform hypothesis testing of structure, from which it is possible to
distinguish between physical objects and artifacts of the reconstruction. Here
we present this new formalism, demonstrate the method on illustrative examples,
before applying the developed formalism to two observational datasets of the
Abel-520 cluster. In our Bayesian framework it is found that neither Abel-520
dataset can conclusively determine the physicality of individual local massive
substructure at significant confidence. However, in both cases the recovered
MAP estimators are consistent with both sets of data
Confidence Propagation through CNNs for Guided Sparse Depth Regression
Generally, convolutional neural networks (CNNs) process data on a regular
grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and
irregularly spaced input data is still an open research problem with numerous
applications in autonomous driving, robotics, and surveillance. In this paper,
we propose an algebraically-constrained normalized convolution layer for CNNs
with highly sparse input that has a smaller number of network parameters
compared to related work. We propose novel strategies for determining the
confidence from the convolution operation and propagating it to consecutive
layers. We also propose an objective function that simultaneously minimizes the
data error while maximizing the output confidence. To integrate structural
information, we also investigate fusion strategies to combine depth and RGB
information in our normalized convolution network framework. In addition, we
introduce the use of output confidence as an auxiliary information to improve
the results. The capabilities of our normalized convolution network framework
are demonstrated for the problem of scene depth completion. Comprehensive
experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The
results clearly demonstrate that the proposed approach achieves superior
performance while requiring only about 1-5% of the number of parameters
compared to the state-of-the-art methods.Comment: 14 pages, 14 Figure
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
FASTLens (FAst STatistics for weak Lensing) : Fast method for Weak Lensing Statistics and map making
With increasingly large data sets, weak lensing measurements are able to
measure cosmological parameters with ever greater precision. However this
increased accuracy also places greater demands on the statistical tools used to
extract the available information. To date, the majority of lensing analyses
use the two point-statistics of the cosmic shear field. These can either be
studied directly using the two-point correlation function, or in Fourier space,
using the power spectrum. But analyzing weak lensing data inevitably involves
the masking out of regions or example to remove bright stars from the field.
Masking out the stars is common practice but the gaps in the data need proper
handling. In this paper, we show how an inpainting technique allows us to
properly fill in these gaps with only operations, leading to a new
image from which we can compute straight forwardly and with a very good
accuracy both the pow er spectrum and the bispectrum. We propose then a new
method to compute the bispectrum with a polar FFT algorithm, which has the main
advantage of avoiding any interpolation in the Fourier domain. Finally we
propose a new method for dark matter mass map reconstruction from shear
observations which integrates this new inpainting concept. A range of examples
based on 3D N-body simulations illustrates the results.Comment: Final version accepted by MNRAS. The FASTLens software is available
from the following link : http://irfu.cea.fr/Ast/fastlens.software.ph
Magnitude Attention-based Dynamic Pruning
Existing pruning methods utilize the importance of each weight based on
specified criteria only when searching for a sparse structure but do not
utilize it during training. In this work, we propose a novel approach -
\textbf{M}agnitude \textbf{A}ttention-based Dynamic \textbf{P}runing (MAP)
method, which applies the importance of weights throughout both the forward and
backward paths to explore sparse model structures dynamically. Magnitude
attention is defined based on the magnitude of weights as continuous
real-valued numbers enabling a seamless transition from a redundant to an
effective sparse network by promoting efficient exploration. Additionally, the
attention mechanism ensures more effective updates for important layers within
the sparse network. In later stages of training, our approach shifts from
exploration to exploitation, exclusively updating the sparse model composed of
crucial weights based on the explored structure, resulting in pruned models
that not only achieve performance comparable to dense models but also
outperform previous pruning methods on CIFAR-10/100 and ImageNet
- …