pwc-archive/papers-with-abstracts huggingface dataset:
Texture Synthesis with Spatial Generative Adversarial Networks
Authors: Nikolay Jetchev, Urs Bergmann, Roland Vollgraf
Date: 2016-11-24 00:00:00
Tasks: Texture Synthesis

Generative adversarial networks (GANs) are a recent approach to train
generative models of data, which have been shown to work particularly well on
image data. In the current paper we introduce a new model for texture synthesis
based on GAN learning. By extending the input noise distribution space from a
single vector to a whole spatial tensor, we create an architecture with
properties well suited to the task of texture synthesis, which we call spatial
GAN (SGAN). To our knowledge, this is the first successful completely
data-driven texture synthesis method based on GANs.
  Our method has the following features which make it a state of the art
algorithm for texture synthesis: high image quality of the generated textures,
very high scalability w.r.t. the output texture size, fast real-time forward
generation, the ability to fuse multiple diverse source images in complex
textures. To illustrate these capabilities we present multiple experiments with
different classes of texture images and use cases. We also discuss some
limitations of our method with respect to the types of texture images it can
synthesize, and compare it to other neural techniques for texture generation.
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds
Authors: Pooria Joulani, András György, Csaba Szepesvári
Date: 2017-09-08 00:00:00
Tasks: Stochastic Optimization

Recently, much work has been done on extending the scope of online learning
and incremental stochastic optimization algorithms. In this paper we contribute
to this effort in two ways: First, based on a new regret decomposition and a
generalization of Bregman divergences, we provide a self-contained, modular
analysis of the two workhorses of online learning: (general) adaptive versions
of Mirror Descent (MD) and the Follow-the-Regularized-Leader (FTRL) algorithms.
The analysis is done with extra care so as not to introduce assumptions not
needed in the proofs and allows to combine, in a straightforward way, different
algorithmic ideas (e.g., adaptivity, optimism, implicit updates) and learning
settings (e.g., strongly convex or composite objectives). This way we are able
to reprove, extend and refine a large body of the literature, while keeping the
proofs concise. The second contribution is a byproduct of this careful
analysis: We present algorithms with improved variational bounds for smooth,
composite objectives, including a new family of optimistic MD algorithms with
only one projection step per round. Furthermore, we provide a simple extension
of adaptive regret bounds to practically relevant non-convex problem settings
with essentially no extra effort.
Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
Authors: Mehmet Turan, Yasin Almalioglu, Helder Araujo, Ender Konukoglu, Metin Sitti
Date: 2017-08-22 00:00:00
Tasks: Diagnostic, Monocular Visual Odometry, Pose Estimation, Visual Odometry

Ingestible wireless capsule endoscopy is an emerging minimally invasive
diagnostic technology for inspection of the GI tract and diagnosis of a wide
range of diseases and pathologies. Medical device companies and many research
groups have recently made substantial progresses in converting passive capsule
endoscopes to active capsule robots, enabling more accurate, precise, and
intuitive detection of the location and size of the diseased areas. Since a
reliable real time pose estimation functionality is crucial for actively
controlled endoscopic capsule robots, in this study, we propose a monocular
visual odometry (VO) method for endoscopic capsule robot operations. Our method
lies on the application of the deep Recurrent Convolutional Neural Networks
(RCNNs) for the visual odometry task, where Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction
and inference of dynamics across the frames, respectively. Detailed analyses
and evaluations made on a real pig stomach dataset proves that our system
achieves high translational and rotational accuracies for different types of
endoscopic capsule robot trajectories.
Entropic Determinants
Authors: Diego Granziol, Stephen Roberts
Date: 2017-09-08 00:00:00

The ability of many powerful machine learning algorithms to deal with large
data sets without compromise is often hampered by computationally expensive
linear algebra tasks, of which calculating the log determinant is a canonical
example. In this paper we demonstrate the optimality of Maximum Entropy methods
in approximating such calculations. We prove the equivalence between mean value
constraints and sample expectations in the big data limit, that Covariance
matrix eigenvalue distributions can be completely defined by moment information
and that the reduction of the self entropy of a maximum entropy proposal
distribution, achieved by adding more moments reduces the KL divergence between
the proposal and true eigenvalue distribution. We empirically verify our
results on a variety of SparseSuite matrices and establish best practices.
Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
Authors: Evgeny Abdulin, Lee Friedman, Oleg V. Komogortsev
Date: 2017-09-08 00:00:00
Tasks: Position

We present software to detect noise in eye position signals from video-based
eye-tracking systems that depend on accurate pupil and corneal reflection
position estimation. When such systems transiently fail to properly detect the
pupil or the corneal reflection due to occlusion from eyelids, eye lashes or
various shadows, the estimated gaze position is false. This produces an
artifactual signal in the position trace that is rapidly, irregularly
oscillating between true and false gaze positions. We refer to this noise as
RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal). Our
method for detecting these periods automatically is based on an estimate of the
relative inefficiency of the eye position signal. We look for RIONEPS in the
horizontal and vertical traces separately, and although we typically use it
offline, it is suitable to adaptation for real time use. This method requires a
threshold to be set, and although we provide some guidance, thresholds will
have to be estimated empirically.
LSTM Fully Convolutional Networks for Time Series Classification
Authors: Fazle Karim, Somshubra Majumdar, Houshang Darabi, Shun Chen
Date: 2017-09-08 00:00:00
Tasks: General Classification, Outlier Detection, Time Series, Time Series Analysis, Time Series Classification

Fully convolutional neural networks (FCN) have been shown to achieve
state-of-the-art performance on the task of classifying time series sequences.
We propose the augmentation of fully convolutional networks with long short
term memory recurrent neural network (LSTM RNN) sub-modules for time series
classification. Our proposed models significantly enhance the performance of
fully convolutional networks with a nominal increase in model size and require
minimal preprocessing of the dataset. The proposed Long Short Term Memory Fully
Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared
to others. We also explore the usage of attention mechanism to improve time
series classification with the Attention Long Short Term Memory Fully
Convolutional Network (ALSTM-FCN). Utilization of the attention mechanism
allows one to visualize the decision process of the LSTM cell. Furthermore, we
propose fine-tuning as a method to enhance the performance of trained models.
An overall analysis of the performance of our model is provided and compared to
other techniques.
Optimizing for Measure of Performance in Max-Margin Parsing
Authors: Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
Date: 2017-09-05 00:00:00
Tasks: Constituency Parsing, Structured Prediction

Many statistical learning problems in the area of natural language processing
including sequence tagging, sequence segmentation and syntactic parsing has
been successfully approached by means of structured prediction methods. An
appealing property of the corresponding discriminative learning algorithms is
their ability to integrate the loss function of interest directly into the
optimization process, which potentially can increase the resulting performance
accuracy. Here, we demonstrate on the example of constituency parsing how to
optimize for F1-score in the max-margin framework of structural SVM. In
particular, the optimization is with respect to the original (not binarized)
trees.
What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation
Authors: Miqing Li, Xin Yao
Date: 2017-09-08 00:00:00

The quality of solution sets generated by decomposition-based evolutionary
multiobjective optimisation (EMO) algorithms depends heavily on the consistency
between a given problem's Pareto front shape and the specified weights'
distribution. A set of weights distributed uniformly in a simplex often lead to
a set of well-distributed solutions on a Pareto front with a simplex-like
shape, but may fail on other Pareto front shapes. It is an open problem on how
to specify a set of appropriate weights without the information of the
problem's Pareto front beforehand. In this paper, we propose an approach to
adapt the weights during the evolutionary process (called AdaW). AdaW
progressively seeks a suitable distribution of weights for the given problem by
elaborating five parts in the weight adaptation --- weight generation, weight
addition, weight deletion, archive maintenance, and weight update frequency.
Experimental results have shown the effectiveness of the proposed approach.
AdaW works well for Pareto fronts with very different shapes: 1) the
simplex-like, 2) the inverted simplex-like, 3) the highly nonlinear, 4) the
disconnect, 5) the degenerated, 6) the badly-scaled, and 7) the
high-dimensional.
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Authors: Han Xiao, Minlie Huang, Yu Hao, Xiaoyan Zhu
Date: 2015-09-18 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Relation

Recently, knowledge graph embedding, which projects symbolic entities and
relations into continuous vector space, has become a new, hot topic in
artificial intelligence. This paper addresses a new issue of multiple relation
semantics that a relation may have multiple meanings revealed by the entity
pairs associated with the corresponding triples, and proposes a novel Gaussian
mixture model for embedding, TransG. The new model can discover latent
semantics for a relation and leverage a mixture of relation component vectors
for embedding a fact triple. To the best of our knowledge, this is the first
generative model for knowledge graph embedding, which is able to deal with
multiple relation semantics. Extensive experiments show that the proposed model
achieves substantial improvements against the state-of-the-art baselines.
Multi-level Feedback Web Links Selection Problem: Learning and Optimization
Authors: Kechao Cai, Kun Chen, Longbo Huang, John C. S. Lui
Date: 2017-09-08 00:00:00

Selecting the right web links for a website is important because appropriate
links not only can provide high attractiveness but can also increase the
website's revenue. In this work, we first show that web links have an intrinsic
\emph{multi-level feedback structure}. For example, consider a $2$-level
feedback web link: the $1$st level feedback provides the Click-Through Rate
(CTR) and the $2$nd level feedback provides the potential revenue, which
collectively produce the compound $2$-level revenue. We consider the
context-free links selection problem of selecting links for a homepage so as to
maximize the total compound $2$-level revenue while keeping the total $1$st
level feedback above a preset threshold. We further generalize the problem to
links with $n~(n\ge2)$-level feedback structure. The key challenge is that the
links' multi-level feedback structures are unobservable unless the links are
selected on the homepage. To our best knowledge, we are the first to model the
links selection problem as a constrained multi-armed bandit problem and design
an effective links selection algorithm by learning the links' multi-level
structure with provable \emph{sub-linear} regret and violation bounds. We
uncover the multi-level feedback structures of web links in two real-world
datasets. We also conduct extensive experiments on the datasets to compare our
proposed \textbf{LExp} algorithm with two state-of-the-art context-free bandit
algorithms and show that \textbf{LExp} algorithm is the most effective in links
selection while satisfying the constraint.
Fuzzy paraphrases in learning word representations with a lexicon
Authors: Yuanzhi Ke, Masafumi Hagiwara
Date: 2016-11-02 00:00:00

A synonym of a polysemous word is usually only the paraphrase of one sense
among many. When lexicons are used to improve vector-space word
representations, such paraphrases are unreliable and bring noise to the
vector-space. The prior works use a coefficient to adjust the overall learning
of the lexicons. They regard the paraphrases equally. In this paper, we propose
a novel approach that regards the paraphrases diversely to alleviate the
adverse effects of polysemy. We annotate each paraphrase with a degree of
reliability. The paraphrases are randomly eliminated according to the degrees
when our model learns word representations. In this way, our approach drops the
unreliable paraphrases, keeping more reliable paraphrases at the same time. The
experimental results show that the proposed method improves the word vectors.
Our approach is an attempt to address the polysemy problem keeping one vector
per word. It makes the approach easier to use than the conventional methods
that estimate multiple vectors for a word. Our approach also outperforms the
prior works in the experiments.
Locating 3D Object Proposals: A Depth-Based Online Approach
Authors: Ramanpreet Singh Pahwa, Jiangbo Lu, Nianjuan Jiang, Tian Tsong Ng, Minh N. Do
Date: 2017-09-08 00:00:00
Tasks: Computational Efficiency, CPU, Object, object-detection, Object Detection, Object Localization

2D object proposals, quickly detected regions in an image that likely contain
an object of interest, are an effective approach for improving the
computational efficiency and accuracy of object detection in color images. In
this work, we propose a novel online method that generates 3D object proposals
in a RGB-D video sequence. Our main observation is that depth images provide
important information about the geometry of the scene. Diverging from the
traditional goal of 2D object proposals to provide a high recall (lots of 2D
bounding boxes near potential objects), we aim for precise 3D proposals. We
leverage on depth information per frame and multi-view scene information to
obtain accurate 3D object proposals. Using efficient but robust registration
enables us to combine multiple frames of a scene in near real time and generate
3D bounding boxes for potential 3D regions of interest. Using standard metrics,
such as Precision-Recall curves and F-measure, we show that the proposed
approach is significantly more accurate than the current state-of-the-art
techniques. Our online approach can be integrated into SLAM based video
processing for quick 3D object localization. Our method takes less than a
second in MATLAB on the UW-RGBD scene dataset on a single thread CPU and thus,
has potential to be used in low-power chips in Unmanned Aerial Vehicles (UAVs),
quadcopters, and drones.
Object-Oriented Knowledge Extraction using Universal Exploiters
Authors: Dmytro Terletskyi
Date: 2017-09-08 00:00:00
Tasks: Object

This paper contains analysis and extension of exploiters-based knowledge
extraction methods, which allow generation of new knowledge, based on the basic
ones. The main achievement of the paper is useful features of some universal
exploiters proof, which allow extending set of basic classes and set of basic
relations by finite set of new classes of objects and relations among them,
which allow creating of complete lattice. Proposed approach gives an
opportunity to compute quantity of new classes, which can be generated using
it, and quantity of different types, which each of obtained classes describes;
constructing of defined hierarchy of classes with determined subsumption
relation; avoidance of some problems of inheritance and more efficient
restoring of basic knowledge within the database.
Calibration of depth cameras using denoised depth images
Authors: Ramanpreet Singh Pahwa, Minh N. Do, Tian Tsong Ng, Binh-Son Hua
Date: 2017-09-08 00:00:00
Tasks: Camera Calibration, Denoising

Depth sensing devices have created various new applications in scientific and
commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing
Device) cameras. Most of these applications require the depth cameras to be
pre-calibrated. However, traditional calibration methods using a checkerboard
do not work very well for depth cameras due to the low image resolution. In
this paper, we propose a depth calibration scheme which excels in estimating
camera calibration parameters when only a handful of corners and calibration
images are available. We exploit the noise properties of PMD devices to denoise
depth measurements and perform camera calibration using the denoised depth as
an additional set of measurements. Our synthetic and real experiments show that
our depth denoising and depth based calibration scheme provides significantly
better results than traditional calibration methods.
Combining Residual Networks with LSTMs for Lipreading
Authors: Themos Stafylakis, Georgios Tzimiropoulos
Date: 2017-03-12 00:00:00
Tasks: Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size target-words consisting of 1.28sec video excerpts from BBC
TV broadcasts. The proposed network attains word accuracy equal to 83.0,
yielding 6.8 absolute improvement over the current state-of-the-art, without
using information about word boundaries during training or testing.
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition
Authors: Matias Valdenegro-Toro
Date: 2017-09-08 00:00:00
Tasks: Transfer Learning

Convolutional Neural Networks (CNN) have revolutionized perception for color
images, and their application to sonar images has also obtained good results.
But in general CNNs are difficult to train without a large dataset, need manual
tuning of a considerable number of hyperparameters, and require many careful
decisions by a designer. In this work, we evaluate three common decisions that
need to be made by a CNN designer, namely the performance of transfer learning,
the effect of object/image size and the relation between training set size. We
evaluate three CNN models, namely one based on LeNet, and two based on the Fire
module from SqueezeNet. Our findings are: Transfer learning with an SVM works
very well, even when the train and transfer sets have no classes in common, and
high classification performance can be obtained even when the target dataset is
small. The ADAM optimizer combined with Batch Normalization can make a high
accuracy CNN classifier, even with small image sizes (16 pixels). At least 50
samples per class are required to obtain $90\%$ test accuracy, and using
Dropout with a small dataset helps improve performance, but Batch Normalization
is better when a large dataset is available.
Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks
Authors: Matias Valdenegro-Toro
Date: 2017-09-08 00:00:00
Tasks: Object, object-detection, Object Detection

Forward-looking sonar can capture high resolution images of underwater
scenes, but their interpretation is complex. Generic object detection in such
images has not been solved, specially in cases of small and unknown objects. In
comparison, detection proposal algorithms have produced top performing object
detectors in real-world color images. In this work we develop a Convolutional
Neural Network that can reliably score objectness of image windows in
forward-looking sonar images and by thresholding objectness, we generate
detection proposals. In our dataset of marine garbage objects, we obtain 94%
recall, generating around 60 proposals per image. The biggest strength of our
method is that it can generalize to previously unseen objects. We show this by
detecting chain links, walls and a wrench without previous training in such
objects. We strongly believe our method can be used for class-independent
object detection, with many real-world applications such as chain following and
mine detection.
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Authors: Aaron S. Jackson, Adrian Bulat, Vasileios Argyriou, Georgios Tzimiropoulos
Date: 2017-03-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Face Reconstruction, Face Alignment, Face Reconstruction, regression

3D face reconstruction is a fundamental Computer Vision problem of
extraordinary difficulty. Current systems often assume the availability of
multiple facial images (sometimes from the same subject) as input, and must
address a number of methodological challenges such as establishing dense
correspondences across large facial poses, expressions, and non-uniform
illumination. In general these methods require complex and inefficient
pipelines for model building and fitting. In this work, we propose to address
many of these limitations by training a Convolutional Neural Network (CNN) on
an appropriate dataset consisting of 2D images and 3D facial models or scans.
Our CNN works with just a single 2D facial image, does not require accurate
alignment nor establishes dense correspondence between images, works for
arbitrary facial poses and expressions, and can be used to reconstruct the
whole 3D facial geometry (including the non-visible parts of the face)
bypassing the construction (during training) and fitting (during testing) of a
3D Morphable Model. We achieve this via a simple CNN architecture that performs
direct regression of a volumetric representation of the 3D facial geometry from
a single 2D image. We also demonstrate how the related task of facial landmark
localization can be incorporated into the proposed framework and help improve
reconstruction quality, especially for the cases of large poses and facial
expressions. Testing code will be made available online, along with pre-trained
models http://aaronsplace.co.uk/papers/jackson2017recon
Towards Proving the Adversarial Robustness of Deep Neural Networks
Authors: Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer
Date: 2017-09-08 00:00:00
Tasks: Adversarial Robustness, Autonomous Vehicles

Autonomous vehicles are highly complex systems, required to function reliably
in a wide variety of situations. Manually crafting software controllers for
these vehicles is difficult, but there has been some success in using deep
neural networks generated using machine-learning. However, deep neural networks
are opaque to human engineers, rendering their correctness very difficult to
prove manually; and existing automated techniques, which were not designed to
operate on neural networks, fail to scale to large systems. This paper focuses
on proving the adversarial robustness of deep neural networks, i.e. proving
that small perturbations to a correctly-classified input to the network cannot
cause it to be misclassified. We describe some of our recent and ongoing work
on verifying the adversarial robustness of networks, and discuss some of the
open questions we have encountered and how they might be addressed.