384 research outputs found
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the model overfits to synthetic images, making the convolutional
filters incompetent to extract informative representation for real images; 2)
there is a distribution difference between synthetic and real data, which is
also known as the domain adaptation problem. To this end, we propose a new
reality oriented adaptation approach for urban scene semantic segmentation by
learning from synthetic data. First, we propose a target guided distillation
approach to learn the real image style, which is achieved by training the
segmentation model to imitate a pretrained real style model using real images.
Second, we further take advantage of the intrinsic spatial structure presented
in urban scene images, and propose a spatial-aware adaptation scheme to
effectively align the distribution of two domains. These two modules can be
readily integrated with existing state-of-the-art semantic segmentation
networks to improve their generalizability when adapting from synthetic to real
urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting
from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness
of our method.Comment: Add experiments on SYNTHIA, CVPR 2018 camera-ready versio
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
It is a highly desirable property for deep networks to be robust against
small input changes. One popular way to achieve this property is by designing
networks with a small Lipschitz constant. In this work, we propose a new
technique for constructing such Lipschitz networks that has a number of
desirable properties: it can be applied to any linear network layer
(fully-connected or convolutional), it provides formal guarantees on the
Lipschitz constant, it is easy to implement and efficient to run, and it can be
combined with any training objective and optimization method. In fact, our
technique is the first one in the literature that achieves all of these
properties simultaneously. Our main contribution is a rescaling-based weight
matrix parametrization that guarantees each network layer to have a Lipschitz
constant of at most 1 and results in the learned weight matrices to be close to
orthogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL).
Experiments and ablation studies in the context of image classification with
certified robust accuracy confirm that AOL layers achieve results that are on
par with most existing methods. Yet, they are simpler to implement and more
broadly applicable, because they do not require computationally expensive
matrix orthogonalization or inversion steps as part of the network
architecture. We provide code at https://github.com/berndprach/AOL.Comment: - Corrected the results from competitor ECO. - Corrected a typo in
the loss function equatio
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
A Self-Consistent Field Solution for Robust Common Spatial Pattern Analysis
The common spatial pattern analysis (CSP) is a widely used signal processing
technique in brain-computer interface (BCI) systems to increase the
signal-to-noise ratio in electroencephalogram (EEG) recordings. Despite its
popularity, the CSP's performance is often hindered by the nonstationarity and
artifacts in EEG signals. The minmax CSP improves the robustness of the CSP by
using data-driven covariance matrices to accommodate the uncertainties. We show
that by utilizing the optimality conditions, the minmax CSP can be recast as an
eigenvector-dependent nonlinear eigenvalue problem (NEPv). We introduce a
self-consistent field (SCF) iteration with line search that solves the NEPv of
the minmax CSP. Local quadratic convergence of the SCF for solving the NEPv is
illustrated using synthetic datasets. More importantly, experiments with
real-world EEG datasets show the improved motor imagery classification rates
and shorter running time of the proposed SCF-based solver compared to the
existing algorithm for the minmax CSP
GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE
In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute
differences (ROAD) and median of the absolute deviations from the median (MAD) is
proposed. The proposed method consists of three components, including fuzzy noise
detection system, fuzzy switching scheme filtering, and fuzzy parameters
optimization using genetic algorithms (GA) to perform efficient and effective noise
removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a
pixel. Fuzzy inference system is used to justify the degree of which a pixel can be
categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme
that adopts median filter as the main estimator is applied to the filtering. The GA
training aims to find the best parameters for the fuzzy sets in the fuzzy noise
detection.
From the experimental results, the proposed method has successfully removed
mixed impulse noise in low to medium probabilities, while keeping the uncorrupted
pixels less affected by the median filtering. It also surpasses the other methods, either
classical or soft computing-based approaches to impulse noise removal, in MAE and
PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise
well
The Kalai-Smorodinski solution for many-objective Bayesian optimization
An ongoing aim of research in multiobjective Bayesian optimization is to
extend its applicability to a large number of objectives. While coping with a
limited budget of evaluations, recovering the set of optimal compromise
solutions generally requires numerous observations and is less interpretable
since this set tends to grow larger with the number of objectives. We thus
propose to focus on a specific solution originating from game theory, the
Kalai-Smorodinsky solution, which possesses attractive properties. In
particular, it ensures equal marginal gains over all objectives. We further
make it insensitive to a monotonic transformation of the objectives by
considering the objectives in the copula space. A novel tailored algorithm is
proposed to search for the solution, in the form of a Bayesian optimization
algorithm: sequential sampling decisions are made based on acquisition
functions that derive from an instrumental Gaussian process prior. Our approach
is tested on four problems with respectively four, six, eight, and nine
objectives. The method is available in the Rpackage GPGame available on CRAN at
https://cran.r-project.org/package=GPGame
Optimization of Alpha-Beta Log-Det Divergences and their Application in the Spatial Filtering of Two Class Motor Imagery Movements
The Alpha-Beta Log-Det divergences for positive definite matrices are flexible divergences
that are parameterized by two real constants and are able to specialize several relevant classical cases
like the squared Riemannian metric, the Steins loss, the S-divergence, etc. A novel classification
criterion based on these divergences is optimized to address the problem of classification of the
motor imagery movements. This research paper is divided into three main sections in order to
address the above mentioned problem: (1) Firstly, it is proven that a suitable scaling of the class
conditional covariance matrices can be used to link the Common Spatial Pattern (CSP) solution with a
predefined number of spatial filters for each class and its representation as a divergence optimization
problem by making their different filter selection policies compatible; (2) A closed form formula for
the gradient of the Alpha-Beta Log-Det divergences is derived that allows to perform optimization
as well as easily use it in many practical applications; (3) Finally, in similarity with the work of
Samek et al. 2014, which proposed the robust spatial filtering of the motor imagery movements based
on the beta-divergence, the optimization of the Alpha-Beta Log-Det divergences is applied to this
problem. The resulting subspace algorithm provides a unified framework for testing the performance
and robustness of the several divergences in different scenarios.Ministerio de Economía y Competitividad TEC2014-53103-
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
Robust topology optimization of three-dimensional photonic-crystal band-gap structures
We perform full 3D topology optimization (in which "every voxel" of the unit
cell is a degree of freedom) of photonic-crystal structures in order to find
optimal omnidirectional band gaps for various symmetry groups, including fcc
(including diamond), bcc, and simple-cubic lattices. Even without imposing the
constraints of any fabrication process, the resulting optimal gaps are only
slightly larger than previous hand designs, suggesting that current photonic
crystals are nearly optimal in this respect. However, optimization can discover
new structures, e.g. a new fcc structure with the same symmetry but slightly
larger gap than the well known inverse opal, which may offer new degrees of
freedom to future fabrication technologies. Furthermore, our band-gap
optimization is an illustration of a computational approach to 3D dispersion
engineering which is applicable to many other problems in optics, based on a
novel semidefinite-program formulation for nonconvex eigenvalue optimization
combined with other techniques such as a simple approach to impose symmetry
constraints. We also demonstrate a technique for \emph{robust} topology
optimization, in which some uncertainty is included in each voxel and we
optimize the worst-case gap, and we show that the resulting band gaps have
increased robustness to systematic fabrication errors.Comment: 17 pages, 9 figures, submitted to Optics Expres
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