pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
Authors: Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh
Date: 2018-04-28 00:00:00
Tasks: Domain Adaptation, Metric Learning, Transfer Learning

We present a novel framework for domain adaptation, whereby both geometric
and statistical differences between a labeled source domain and unlabeled
target domain can be integrated by exploiting the curved Riemannian geometry of
statistical manifolds. Our approach is based on formulating transfer from
source to target as a problem of geometric mean metric learning on manifolds.
Specifically, we exploit the curved Riemannian manifold geometry of symmetric
positive definite (SPD) covariance matrices. We exploit a simple but important
observation that as the space of covariance matrices is both a Riemannian space
as well as a homogeneous space, the shortest path geodesic between two
covariances on the manifold can be computed analytically. Statistics on the SPD
matrix manifold, such as the geometric mean of two matrices can be reduced to
solving the well-known Riccati equation. We show how the Ricatti-based solution
can be constrained to not only reduce the statistical differences between the
source and target domains, such as aligning second order covariances and
minimizing the maximum mean discrepancy, but also the underlying geometry of
the source and target domains using diffusions on the underlying source and
target manifolds. A key strength of our proposed approach is that it enables
integrating multiple sources of variation between source and target in a
unified way, by reducing the combined objective function to a nested set of
Ricatti equations where the solution can be represented by a cascaded series of
geometric mean computations. In addition to showing the theoretical optimality
of our solution, we present detailed experiments using standard transfer
learning testbeds from computer vision comparing our proposed algorithms to
past work in domain adaptation, showing improved results over a large variety
of previous methods.
On Euclidean $k$-Means Clustering with $α$-Center Proximity
Authors: Amit Deshpande, Anand Louis, Apoorv Vikram Singh
Date: 2018-04-28 00:00:00
Tasks: Clustering

$k$-means clustering is NP-hard in the worst case but previous work has shown
efficient algorithms assuming the optimal $k$-means clusters are \emph{stable}
under additive or multiplicative perturbation of data. This has two caveats.
First, we do not know how to efficiently verify this property of optimal
solutions that are NP-hard to compute in the first place. Second, the stability
assumptions required for polynomial time $k$-means algorithms are often
unreasonable when compared to the ground-truth clusters in real-world data. A
consequence of multiplicative perturbation resilience is \emph{center
proximity}, that is, every point is closer to the center of its own cluster
than the center of any other cluster, by some multiplicative factor $\alpha >
1$.
  We study the problem of minimizing the Euclidean $k$-means objective only
over clusterings that satisfy $\alpha$-center proximity. We give a simple
algorithm to find the optimal $\alpha$-center-proximal $k$-means clustering in
running time exponential in $k$ and $1/(\alpha - 1)$ but linear in the number
of points and the dimension. We define an analogous $\alpha$-center proximity
condition for outliers, and give similar algorithmic guarantees for $k$-means
with outliers and $\alpha$-center proximity. On the hardness side we show that
for any $\alpha' > 1$, there exists an $\alpha \leq \alpha'$, $(\alpha >1)$,
and an $\varepsilon_0 > 0$ such that minimizing the $k$-means objective over
clusterings that satisfy $\alpha$-center proximity is NP-hard to approximate
within a multiplicative $(1+\varepsilon_0)$ factor.
A Bimodal Learning Approach to Assist Multi-sensory Effects Synchronization
Authors: Raphael Abreu, Joel dos Santos, Eduardo Bezerra
Date: 2018-04-28 00:00:00

In mulsemedia applications, traditional media content (text, image, audio,
video, etc.) can be related to media objects that target other human senses
(e.g., smell, haptics, taste). Such applications aim at bridging the virtual
and real worlds through sensors and actuators. Actuators are responsible for
the execution of sensory effects (e.g., wind, heat, light), which produce
sensory stimulations on the users. In these applications sensory stimulation
must happen in a timely manner regarding the other traditional media content
being presented. For example, at the moment in which an explosion is presented
in the audiovisual content, it may be adequate to activate actuators that
produce heat and light. It is common to use some declarative multimedia
authoring language to relate the timestamp in which each media object is to be
presented to the execution of some sensory effect. One problem in this setting
is that the synchronization of media objects and sensory effects is done
manually by the author(s) of the application, a process which is time-consuming
and error prone. In this paper, we present a bimodal neural network
architecture to assist the synchronization task in mulsemedia applications. Our
approach is based on the idea that audio and video signals can be used
simultaneously to identify the timestamps in which some sensory effect should
be executed. Our learning architecture combines audio and video signals for the
prediction of scene components. For evaluation purposes, we construct a dataset
based on Google's AudioSet. We provide experiments to validate our bimodal
architecture. Our results show that the bimodal approach produces better
results when compared to several variants of unimodal architectures.
On Convergence of Moments for Approximating Processes and Applications to Surrogate Models
Authors: Ansgar Steland
Date: 2018-04-28 00:00:00

We study critera for a pair $ (\{ X_n \} $, $ \{ Y_n \}) $ of approximating
processes which guarantee closeness of moments by generalizing known results
for the special case that $ Y_n = Y $ for all $n$ and $ X_n $ converges to $Y$
in probability. This problem especially arises when working with surrogate
models, e.g. to enrich observed data by simulated data, where the surrogates
$Y_n$'s are constructed to justify that they approximate the $ X_n $'s.
  The results of this paper deal with sequences of random variables. Since this
framework does not cover many applications where surrogate models such as deep
neural networks are used to approximate more general stochastic processes, we
extend the results to the more general framework of random fields of stochastic
processes. This framework especially covers image data and sequences of images.
We show that uniform integrability is sufficient, and this holds even for the
case of processes provided they satisfy a weak stationarity condition.
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
Authors: Sounak Dey, Anjan Dutta, Suman K. Ghosh, Ernest Valveny, Josep Lladós, Umapada Pal
Date: 2018-04-28 00:00:00
Tasks: Image Retrieval, Retrieval

In this work we introduce a cross modal image retrieval system that allows
both text and sketch as input modalities for the query. A cross-modal deep
network architecture is formulated to jointly model the sketch and text input
modalities as well as the the image output modality, learning a common
embedding between text and images and between sketches and images. In addition,
an attention model is used to selectively focus the attention on the different
objects of the image, allowing for retrieval with multiple objects in the
query. Experiments show that the proposed method performs the best in both
single and multiple object image retrieval in standard datasets.
A Logic of Agent Organizations
Authors: Virginia Dignum, Frank Dignum
Date: 2018-04-28 00:00:00

Organization concepts and models are increasingly being adopted for the
design and specification of multi-agent systems. Agent organizations can be
seen as mechanisms of social order, created to achieve global (or
organizational) objectives by more or less autonomous agents. In order to
develop a theory on the relation between organizational structures,
organizational objectives and the actions of agents fulfilling roles in the
organization a theoretical framework is needed to describe organizational
structures and actions of (groups of) agents. Current logical formalisms focus
on specific aspects of organizations (e.g. power, delegation, agent actions, or
normative issues) but a framework that integrates and relates different aspects
is missing. Given the amount of aspects involved and the subsequent complexity
of a formalism encompassing them all, it is difficult to realize. In this
paper, a first step is taken to solve this problem. We present a generic formal
model that enables to specify and relate the main concepts of an organization
(including, activity, structure, environment and others) so that organizations
can be analyzed at a high level of abstraction. However, for some aspects we
use a simplified model in order to avoid the complexity of combining many
different types of (modal) operators.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Authors: Naji Khosravan, Haydar Celik, Baris Turkbey, Elizabeth Jones, Bradford Wood, Ulas Bagci
Date: 2018-02-17 00:00:00
Tasks: Clustering, Diagnostic, Multi-Task Learning

There are at least two categories of errors in radiology screening that can
lead to suboptimal diagnostic decisions and interventions:(i)human fallibility
and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are
developed to help radiologists to compensate for some of these errors. However,
despite their significant improvements over conventional screening strategies,
most CAD systems do not go beyond their use as second opinion tools due to
producing a high number of false positives, which human interpreters need to
correct. In parallel with efforts in computerized analysis of radiology scans,
several researchers have examined behaviors of radiologists while screening
medical images to better understand how and why they miss tumors, how they
interact with the information in an image, and how they search for unknown
pathology in the images. Eye-tracking tools have been instrumental in exploring
answers to these fundamental questions. In this paper, we aim to develop a
paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both
of the above mentioned research lines: CAD and eye-tracking. We design an
eye-tracking interface providing radiologists with a real radiology reading
room experience. Then, we propose a novel algorithm that unifies eye-tracking
data and a CAD system. Specifically, we present a new graph based clustering
and sparsification algorithm to transform eye-tracking data (gaze) into a
signal model to interpret gaze patterns quantitatively and qualitatively. The
proposed C-CAD collaborates with radiologists via eye-tracking technology and
helps them to improve diagnostic decisions. The C-CAD learns radiologists'
search efficiency by processing their gaze patterns. To do this, the C-CAD uses
a deep learning algorithm in a newly designed multi-task learning platform to
segment and diagnose cancers simultaneously.
Safety-Aware Apprenticeship Learning
Authors: Weichao Zhou, Wenchao Li
Date: 2017-10-22 00:00:00

Apprenticeship learning (AL) is a kind of Learning from Demonstration
techniques where the reward function of a Markov Decision Process (MDP) is
unknown to the learning agent and the agent has to derive a good policy by
observing an expert's demonstrations. In this paper, we study the problem of
how to make AL algorithms inherently safe while still meeting its learning
objective. We consider a setting where the unknown reward function is assumed
to be a linear combination of a set of state features, and the safety property
is specified in Probabilistic Computation Tree Logic (PCTL). By embedding
probabilistic model checking inside AL, we propose a novel
counterexample-guided approach that can ensure safety while retaining
performance of the learnt policy. We demonstrate the effectiveness of our
approach on several challenging AL scenarios where safety is essential.
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
Authors: Muhammet Bastan, Kim-Hui Yap, Lap-Pui Chau
Date: 2018-04-28 00:00:00
Tasks: Event Detection, vehicle detection

Idling vehicles waste energy and pollute the environment through exhaust
emission. In some countries, idling a vehicle for more than a predefined
duration is prohibited and automatic idling vehicle detection is desirable for
law enforcement. We propose the first automatic system to detect idling cars,
using infrared (IR) imaging and deep networks.
  We rely on the differences in spatio-temporal heat signatures of idling and
stopped cars and monitor the car temperature with a long-wavelength IR camera.
We formulate the idling car detection problem as spatio-temporal event
detection in IR image sequences and employ deep networks for spatio-temporal
modeling. We collected the first IR image sequence dataset for idling car
detection. First, we detect the cars in each IR image using a convolutional
neural network, which is pre-trained on regular RGB images and fine-tuned on IR
images for higher accuracy. Then, we track the detected cars over time to
identify the cars that are parked. Finally, we use the 3D spatio-temporal IR
image volume of each parked car as input to convolutional and recurrent
networks to classify them as idling or not. We carried out an extensive
empirical evaluation of temporal and spatio-temporal modeling approaches with
various convolutional and recurrent architectures. We present promising
experimental results on our IR image sequence dataset.
Joint Enhancement and Denoising Method via Sequential Decomposition
Authors: Xutong Ren, Mading Li, Wen-Huang Cheng, Jiaying Liu
Date: 2018-04-23 00:00:00
Tasks: Denoising

Many low-light enhancement methods ignore intensive noise in original images.
As a result, they often simultaneously enhance the noise as well. Furthermore,
extra denoising procedures adopted by most methods ruin the details. In this
paper, we introduce a joint low-light enhancement and denoising strategy, aimed
at obtaining well-enhanced low-light images while getting rid of the inherent
noise issue simultaneously. The proposed method performs Retinex model based
decomposition in a successive sequence, which sequentially estimates a
piece-wise smoothed illumination and a noise-suppressed reflectance. After
getting the illumination and reflectance map, we adjust the illumination layer
and generate our enhancement result. In this noise-suppressed sequential
decomposition process we enforce the spatial smoothness on each component and
skillfully make use of weight matrices to suppress the noise and improve the
contrast. Results of extensive experiments demonstrate the effectiveness and
practicability of our method. It performs well for a wide variety of images,
and achieves better or comparable quality compared with the state-of-the-art
methods.
Quantitatively Evaluating GANs With Divergences Proposed for Training
Authors: Daniel Jiwoong Im, He Ma, Graham Taylor, Kristin Branson
Date: 2018-03-02 00:00:00
Proceeding: ICLR 2018 1

Generative adversarial networks (GANs) have been extremely effective in
approximating complex distributions of high-dimensional, input data samples,
and substantial progress has been made in understanding and improving GAN
performance in terms of both theory and application. However, we currently lack
quantitative methods for model assessment. Because of this, while many GAN
variants are being proposed, we have relatively little understanding of their
relative abilities. In this paper, we evaluate the performance of various types
of GANs using divergence and distance functions typically used only for
training. We observe consistency across the various proposed metrics and,
interestingly, the test-time metrics do not favour networks that use the same
training-time criterion. We also compare the proposed metrics to human
perceptual scores.
Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization
Authors: Risheng Liu, Shichao Cheng, Yi He, Xin Fan, Zhongxuan Luo
Date: 2018-04-28 00:00:00
Tasks: Deblurring

Operator splitting methods have been successfully used in computational
sciences, statistics, learning and vision areas to reduce complex problems into
a series of simpler subproblems. However, prevalent splitting schemes are
mostly established only based on the mathematical properties of some general
optimization models. So it is a laborious process and often requires many
iterations of ideation and validation to obtain practical and task-specific
optimal solutions, especially for nonconvex problems in real-world scenarios.
To break through the above limits, we introduce a new algorithmic framework,
called Learnable Bregman Splitting (LBS), to perform deep-architecture-based
operator splitting for nonconvex optimization based on specific task model.
Thanks to the data-dependent (i.e., learnable) nature, our LBS can not only
speed up the convergence, but also avoid unwanted trivial solutions for
real-world tasks. Though with inexact deep iterations, we can still establish
the global convergence and estimate the asymptotic convergence rate of LBS only
by enforcing some fairly loose assumptions. Extensive experiments on different
applications (e.g., image completion and deblurring) verify our theoretical
results and show the superiority of LBS against existing methods.
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs
Authors: Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Bjoern Andres, Daniel Cremers
Date: 2017-05-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper introduces a novel algorithm for transductive inference in
higher-order MRFs, where the unary energies are parameterized by a variable
classifier. The considered task is posed as a joint optimization problem in the
continuous classifier parameters and the discrete label variables. In contrast
to prior approaches such as convex relaxations, we propose an advantageous
decoupling of the objective function into discrete and continuous subproblems
and a novel, efficient optimization method related to ADMM. This approach
preserves integrality of the discrete label variables and guarantees global
convergence to a critical point. We demonstrate the advantages of our approach
in several experiments including video object segmentation on the DAVIS data
set and interactive image segmentation.
Credit risk prediction in an imbalanced social lending environment
Authors: Anahita Namvar, Mohammad Siami, Fethi Rabhi, Mohsen Naderpour
Date: 2018-04-28 00:00:00
Tasks: Prediction

Credit risk prediction is an effective way of evaluating whether a potential
borrower will repay a loan, particularly in peer-to-peer lending where class
imbalance problems are prevalent. However, few credit risk prediction models
for social lending consider imbalanced data and, further, the best resampling
technique to use with imbalanced data is still controversial. In an attempt to
address these problems, this paper presents an empirical comparison of various
combinations of classifiers and resampling techniques within a novel risk
assessment methodology that incorporates imbalanced data. The credit
predictions from each combination are evaluated with a G-mean measure to avoid
bias towards the majority class, which has not been considered in similar
studies. The results reveal that combining random forest and random
under-sampling may be an effective strategy for calculating the credit risk
associated with loan applicants in social lending markets.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Authors: Rayson Laroca, Evair Severo, Luiz A. Zanlorensi, Luiz S. Oliveira, Gabriel Resende Gonçalves, William Robson Schwartz, David Menotti
Date: 2018-02-26 00:00:00
Tasks: Data Augmentation, License Plate Detection, License Plate Recognition, Optical Character Recognition (OCR)

Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.
SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
Authors: Sungeun Hong, Woobin Im, Jongbin Ryu, Hyun S. Yang
Date: 2017-02-14 00:00:00
Tasks: Domain Adaptation, Face Model, Face Recognition, Image Generation

Real-world face recognition using a single sample per person (SSPP) is a
challenging task. The problem is exacerbated if the conditions under which the
gallery image and the probe set are captured are completely different. To
address these issues from the perspective of domain adaptation, we introduce an
SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain
adaptation, feature extraction, and classification are performed jointly using
a deep architecture with domain-adversarial training. However, the SSPP
characteristic of one training sample per class is insufficient to train the
deep architecture. To overcome this shortage, we generate synthetic images with
varying poses using a 3D face model. Experimental evaluations using a realistic
SSPP dataset show that deep domain adaptation and image synthesis complement
each other and dramatically improve accuracy. Experiments on a benchmark
dataset using the proposed approach show state-of-the-art performance. All the
dataset and the source code can be found in our online repository
(https://github.com/csehong/SSPP-DAN).