7,588 research outputs found
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly.Comment: CVPR 2018 spotligh
Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis
This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector
Towards Precision LSST Weak-Lensing Measurement - I: Impacts of Atmospheric Turbulence and Optical Aberration
The weak-lensing science of the LSST project drives the need to carefully
model and separate the instrumental artifacts from the intrinsic lensing
signal. The dominant source of the systematics for all ground based telescopes
is the spatial correlation of the PSF modulated by both atmospheric turbulence
and optical aberrations. In this paper, we present a full FOV simulation of the
LSST images by modeling both the atmosphere and the telescope optics with the
most current data for the telescope specifications and the environment. To
simulate the effects of atmospheric turbulence, we generated six-layer phase
screens with the parameters estimated from the on-site measurements. For the
optics, we combined the ray-tracing tool ZEMAX and our simulated focal plane
data to introduce realistic aberrations and focal plane height fluctuations.
Although this expected flatness deviation for LSST is small compared with that
of other existing cameras, the fast f-ratio of the LSST optics makes this focal
plane flatness variation and the resulting PSF discontinuities across the CCD
boundaries significant challenges in our removal of the systematics. We resolve
this complication by performing PCA CCD-by-CCD, and interpolating the basis
functions using conventional polynomials. We demonstrate that this PSF
correction scheme reduces the residual PSF ellipticity correlation below 10^-7
over the cosmologically interesting scale. From a null test using HST/UDF
galaxy images without input shear, we verify that the amplitude of the galaxy
ellipticity correlation function, after the PSF correction, is consistent with
the shot noise set by the finite number of objects. Therefore, we conclude that
the current optical design and specification for the accuracy in the focal
plane assembly are sufficient to enable the control of the PSF systematics
required for weak-lensing science with the LSST.Comment: Accepted to PASP. High-resolution version is available at
http://dls.physics.ucdavis.edu/~mkjee/LSST_weak_lensing_simulation.pd
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
The rise of graph-structured data such as social networks, regulatory
networks, citation graphs, and functional brain networks, in combination with
resounding success of deep learning in various applications, has brought the
interest in generalizing deep learning models to non-Euclidean domains. In this
paper, we introduce a new spectral domain convolutional architecture for deep
learning on graphs. The core ingredient of our model is a new class of
parametric rational complex functions (Cayley polynomials) allowing to
efficiently compute spectral filters on graphs that specialize on frequency
bands of interest. Our model generates rich spectral filters that are localized
in space, scales linearly with the size of the input data for
sparsely-connected graphs, and can handle different constructions of Laplacian
operators. Extensive experimental results show the superior performance of our
approach, in comparison to other spectral domain convolutional architectures,
on spectral image classification, community detection, vertex classification
and matrix completion tasks
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