2,491 research outputs found
Compression of Deep Neural Networks on the Fly
Thanks to their state-of-the-art performance, deep neural networks are
increasingly used for object recognition. To achieve these results, they use
millions of parameters to be trained. However, when targeting embedded
applications the size of these models becomes problematic. As a consequence,
their usage on smartphones or other resource limited devices is prohibited. In
this paper we introduce a novel compression method for deep neural networks
that is performed during the learning phase. It consists in adding an extra
regularization term to the cost function of fully-connected layers. We combine
this method with Product Quantization (PQ) of the trained weights for higher
savings in storage consumption. We evaluate our method on two data sets (MNIST
and CIFAR10), on which we achieve significantly larger compression rates than
state-of-the-art methods
Optimal Compression of Floating-point Astronomical Images Without Significant Loss of Information
We describe a compression method for floating-point astronomical images that
gives compression ratios of 6 -- 10 while still preserving the scientifically
important information in the image. The pixel values are first preprocessed by
quantizing them into scaled integer intensity levels, which removes some of the
uncompressible noise in the image. The integers are then losslessly compressed
using the fast and efficient Rice algorithm and stored in a portable FITS
format file. Quantizing an image more coarsely gives greater image compression,
but it also increases the noise and degrades the precision of the photometric
and astrometric measurements in the quantized image. Dithering the pixel values
during the quantization process can greatly improve the precision of
measurements in the images. This is especially important if the analysis
algorithm relies on the mode or the median which would be similarly quantized
if the pixel values are not dithered. We perform a series of experiments on
both synthetic and real astronomical CCD images to quantitatively demonstrate
that the magnitudes and positions of stars in the quantized images can be
measured with the predicted amount of precision. In order to encourage wider
use of these image compression methods, we have made available a pair of
general-purpose image compression programs, called fpack and funpack, which can
be used to compress any FITS format image.Comment: Accepted PAS
Anisotropic Particles Strengthen Granular Pillars under Compression
We probe the effects of particle shape on the global and local behavior of a
two-dimensional granular pillar, acting as a proxy for a disordered solid,
under uniaxial compression. This geometry allows for direct measurement of
global material response, as well as tracking of all individual particle
trajectories. In general, drawing connections between local structure and local
dynamics can be challenging in amorphous materials due to lower precision of
atomic positions, so this study aims to elucidate such connections. We vary
local interactions by using three different particle shapes: discrete circular
grains (monomers), pairs of grains bonded together (dimers), and groups of
three bonded in a triangle (trimers). We find that dimers substantially
strengthen the pillar and the degree of this effect is determined by
orientational order in the initial condition. In addition, while the three
particle shapes form void regions at distinct rates, we find that anisotropies
in the local amorphous structure remain robust through the definition of a
metric that quantifies packing anisotropy. Finally, we highlight connections
between local deformation rates and local structure.Comment: 15 pages, 15 figure
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
In this paper we present an efficient method for visual descriptors retrieval
based on compact hash codes computed using a multiple k-means assignment. The
method has been applied to the problem of approximate nearest neighbor (ANN)
search of local and global visual content descriptors, and it has been tested
on different datasets: three large scale public datasets of up to one billion
descriptors (BIGANN) and, supported by recent progress in convolutional neural
networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results
show that, despite its simplicity, the proposed method obtains a very high
performance that makes it superior to more complex state-of-the-art methods
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