pwc-archive/papers-with-abstracts huggingface dataset:
MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer
Authors: Gino Brunner, Andres Konrad, Yuyi Wang, Roger Wattenhofer
Date: 2018-09-20 00:00:00
Tasks: Style Transfer

We introduce MIDI-VAE, a neural network model based on Variational
Autoencoders that is capable of handling polyphonic music with multiple
instrument tracks, as well as modeling the dynamics of music by incorporating
note durations and velocities. We show that MIDI-VAE can perform style transfer
on symbolic music by automatically changing pitches, dynamics and instruments
of a music piece from, e.g., a Classical to a Jazz style. We evaluate the
efficacy of the style transfer by training separate style validation
classifiers. Our model can also interpolate between short pieces of music,
produce medleys and create mixtures of entire songs. The interpolations
smoothly change pitches, dynamics and instrumentation to create a harmonic
bridge between two music pieces. To the best of our knowledge, this work
represents the first successful attempt at applying neural style transfer to
complete musical compositions.
Sparsified SGD with Memory
Authors: Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
Date: 2018-09-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Distributed Optimization, Quantization

Huge scale machine learning problems are nowadays tackled by distributed
optimization algorithms, i.e. algorithms that leverage the compute power of
many devices for training. The communication overhead is a key bottleneck that
hinders perfect scalability. Various recent works proposed to use quantization
or sparsification techniques to reduce the amount of data that needs to be
communicated, for instance by only sending the most significant entries of the
stochastic gradient (top-k sparsification). Whilst such schemes showed very
promising performance in practice, they have eluded theoretical analysis so
far.
  In this work we analyze Stochastic Gradient Descent (SGD) with
k-sparsification or compression (for instance top-k or random-k) and show that
this scheme converges at the same rate as vanilla SGD when equipped with error
compensation (keeping track of accumulated errors in memory). That is,
communication can be reduced by a factor of the dimension of the problem
(sometimes even more) whilst still converging at the same rate. We present
numerical experiments to illustrate the theoretical findings and the better
scalability for distributed applications.
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
Authors: Roberto Interdonato, Dino Ienco, Raffaele Gaetano, Kenji Ose
Date: 2018-09-20 00:00:00
Tasks: Deep Learning, Earth Observation, General Classification, Land Cover Classification, Time Series, Time Series Analysis, Time Series Classification

Nowadays, modern Earth Observation systems continuously generate huge amounts
of data. A notable example is represented by the Sentinel-2 mission, which
provides images at high spatial resolution (up to 10m) with high temporal
revisit period (every 5 days), which can be organized in Satellite Image Time
Series (SITS). While the use of SITS has been proved to be beneficial in the
context of Land Use/Land Cover (LULC) map generation, unfortunately, machine
learning approaches commonly leveraged in remote sensing field fail to take
advantage of spatio-temporal dependencies present in such data.
  Recently, new generation deep learning methods allowed to significantly
advance research in this field. These approaches have generally focused on a
single type of neural network, i.e., Convolutional Neural Networks (CNNs) or
Recurrent Neural Networks (RNNs), which model different but complementary
information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs).
In this work, we propose the first deep learning architecture for the analysis
of SITS data, namely \method{} (DUal view Point deep Learning architecture for
time series classificatiOn), that combines Convolutional and Recurrent neural
networks to exploit their complementarity. Our hypothesis is that, since CNNs
and RNNs capture different aspects of the data, a combination of both models
would produce a more diverse and complete representation of the information for
the underlying land cover classification task. Experiments carried out on two
study sites characterized by different land cover characteristics (i.e., the
\textit{Gard} site in France and the \textit{Reunion Island} in the Indian
Ocean), demonstrate the significance of our proposal.
A Fast and Accurate System for Face Detection, Identification, and Verification
Authors: Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
Date: 2018-09-20 00:00:00
Tasks: Face Detection, Face Identification, Face Recognition, object-detection, Object Detection, Robust Face Recognition

The availability of large annotated datasets and affordable computation power
have led to impressive improvements in the performance of CNNs on various
object detection and recognition benchmarks. These, along with a better
understanding of deep learning methods, have also led to improved capabilities
of machine understanding of faces. CNNs are able to detect faces, locate facial
landmarks, estimate pose, and recognize faces in unconstrained images and
videos. In this paper, we describe the details of a deep learning pipeline for
unconstrained face identification and verification which achieves
state-of-the-art performance on several benchmark datasets. We propose a novel
face detector, Deep Pyramid Single Shot Face Detector (DPSSD), which is fast
and capable of detecting faces with large scale variations (especially tiny
faces). We give design details of the various modules involved in automatic
face recognition: face detection, landmark localization and alignment, and face
identification/verification. We provide evaluation results of the proposed face
detector on challenging unconstrained face detection datasets. Then, we present
experimental results for IARPA Janus Benchmarks A, B and C (IJB-A, IJB-B,
IJB-C), and the Janus Challenge Set 5 (CS5).
Symbolic Music Genre Transfer with CycleGAN
Authors: Gino Brunner, Yuyi Wang, Roger Wattenhofer, Sumu Zhao
Date: 2018-09-20 00:00:00
Tasks: Music Genre Transfer, Style Transfer

Deep generative models such as Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) have recently been applied to style and domain
transfer for images, and in the case of VAEs, music. GAN-based models employing
several generators and some form of cycle consistency loss have been among the
most successful for image domain transfer. In this paper we apply such a model
to symbolic music and show the feasibility of our approach for music genre
transfer. Evaluations using separate genre classifiers show that the style
transfer works well. In order to improve the fidelity of the transformed music,
we add additional discriminators that cause the generators to keep the
structure of the original music mostly intact, while still achieving strong
genre transfer. Visual and audible results further show the potential of our
approach. To the best of our knowledge, this paper represents the first
application of GANs to symbolic music domain transfer.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Authors: Betty van Aken, Julian Risch, Ralf Krestel, Alexander Löser
Date: 2018-09-20 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Toxic Comment Classification

Toxic comment classification has become an active research field with many
recently proposed approaches. However, while these approaches address some of
the task's challenges others still remain unsolved and directions for further
research are needed. To this end, we compare different deep learning and
shallow approaches on a new, large comment dataset and propose an ensemble that
outperforms all individual models. Further, we validate our findings on a
second dataset. The results of the ensemble enable us to perform an extensive
error analysis, which reveals open challenges for state-of-the-art methods and
directions towards pending future research. These challenges include missing
paradigmatic context and inconsistent dataset labels.
RGBD2lux: Dense light intensity estimation with an RGBD sensor
Authors: Theodore Tsesmelis, Irtiza Hasan, Marco Cristani, Fabio Galasso, Alessio Del Bue
Date: 2018-09-20 00:00:00

Lighting design and modelling or industrial applications like luminaire
planning and commissioning rely heavily on time consuming manual measurements
or on physically coherent computational simulations. Regarding the
latter,standard approaches are based on CAD modeling simulations and offline
rendering, with long processing times and therefore inflexible workflows. Thus,
in this paper we pro-pose a computer vision based system to measure lighting
with just a single RGBD camera. The proposed method uses both depth data and
images from the sensor to provide a dense measure of light intensity in the
field of view of the camera. We evaluate our system on novel ground truth data
and compare it to state-of-the-art commercial light-planning software. Our
system provides improved performance, while being completely automated, given
that the CAD model is extracted from the depth and the albedo estimated with
the support of RGB images. To the best of our knowledge, this is the first
automatic framework for the estimation of lighting in general indoor scenarios
from RGBDinput.
On the self-similarity of line segments in decaying homogeneous isotropic turbulence
Authors: Michael Gauding, Lipo Wang, Jens Henrik Goebbert, Mathis Bode, Luminita Danaila, Emilien Varea
Date: 2018-09-20 00:00:00

The self-similarity of a passive scalar in homogeneous isotropic decaying
turbulence is investigated by the method of line segments (M. Gauding et al.,
Physics of Fluids 27.9 (2015): 095102). The analysis is based on a highly
resolved direct numerical simulation of decaying turbulence. The method of line
segments is used to perform a decomposition of the scalar field into smaller
sub-units based on the extremal points of the scalar along a straight line.
These sub-units (the so-called line segments) are parameterized by their length
$\ell$ and the difference $\Delta\phi$ of the scalar field between the ending
points. Line segments can be understood as thin local convective-diffusive
structures in which diffusive processes are enhanced by compressive strain.
From DNS, it is shown that the marginal distribution function of the
length~$\ell$ assumes complete self-similarity when re-scaled by the mean
length $\ell_m$. The joint statistics of $\Delta\phi$ and $\ell$, from which
the local gradient $g=\Delta\phi/\ell$ can be defined, play an important role
in understanding the turbulence mixing and flow structure. Large values of $g$
occur at a small but finite length scale. Statistics of $g$ are characterized
by rare but strong deviations that exceed the standard deviation by more than
one order of magnitude. It is shown that these events break complete
self-similarity of line segments, which confirms the standard paradigm of
turbulence that intense events (which are known as internal intermittency) are
not self-similar.
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Authors: Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, Lihi Zelnik-Manor
Date: 2018-09-20 00:00:00
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution, Video Quality Assessment

This paper reports on the 2018 PIRM challenge on perceptual super-resolution
(SR), held in conjunction with the Perceptual Image Restoration and
Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR
challenges, our evaluation methodology jointly quantifies accuracy and
perceptual quality, therefore enabling perceptual-driven methods to compete
alongside algorithms that target PSNR maximization. Twenty-one participating
teams introduced algorithms which well-improved upon the existing
state-of-the-art methods in perceptual SR, as confirmed by a human opinion
study. We also analyze popular image quality measures and draw conclusions
regarding which of them correlates best with human opinion scores. We conclude
with an analysis of the current trends in perceptual SR, as reflected from the
leading submissions.
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
Authors: Simon Duque Anton, Lia Ahrens, Daniel Fraunholz, Hans Dieter Schotten
Date: 2018-09-20 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection, Time Series, Time Series Analysis

The Industrial Internet of Things drastically increases connectivity of
devices in industrial applications. In addition to the benefits in efficiency,
scalability and ease of use, this creates novel attack surfaces. Historically,
industrial networks and protocols do not contain means of security, such as
authentication and encryption, that are made necessary by this development.
Thus, industrial IT-security is needed. In this work, emulated industrial
network data is transformed into a time series and analysed with three
different algorithms. The data contains labeled attacks, so the performance can
be evaluated. Matrix Profiles perform well with almost no parameterisation
needed. Seasonal Autoregressive Integrated Moving Average performs well in the
presence of noise, requiring parameterisation effort. Long Short Term
Memory-based neural networks perform mediocre while requiring a high training-
and parameterisation effort.
MASON: A Model AgnoStic ObjectNess Framework
Authors: K J Joseph, Vineeth N. Balasubramanian
Date: 2018-09-20 00:00:00
Tasks: model

This paper proposes a simple, yet very effective method to localize dominant
foreground objects in an image, to pixel-level precision. The proposed method
'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to
generate category-independent and model-agnostic heat maps for any image. The
network is not explicitly trained for the task, and hence, can be used
off-the-shelf in tandem with any other network or task. We show that this
framework scales to a wide variety of images, and illustrate the effectiveness
of MASON in three varied application contexts.
Learning a Local Feature Descriptor for 3D LiDAR Scans
Authors: Ayush Dewan, Tim Caselitz, Wolfram Burgard
Date: 2018-09-20 00:00:00
Tasks: Metric Learning

Robust data association is necessary for virtually every SLAM system and
finding corresponding points is typically a preprocessing step for scan
alignment algorithms. Traditionally, handcrafted feature descriptors were used
for these problems but recently learned descriptors have been shown to perform
more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
scans. The descriptor is learned using a Convolutional Neural Network (CNN).
Our proposed architecture consists of a Siamese network for learning a feature
descriptor and a metric learning network for matching the descriptors. We also
present a method for estimating local surface patches and obtaining
ground-truth correspondences. In extensive experiments, we compare our learned
feature descriptor with existing 3D local descriptors and report highly
competitive results for multiple experiments in terms of matching accuracy and
computation time. \end{abstract}
OxIOD: The Dataset for Deep Inertial Odometry
Authors: Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni
Date: 2018-09-20 00:00:00

Advances in micro-electro-mechanical (MEMS) techniques enable inertial
measurements units (IMUs) to be small, cheap, energy efficient, and widely used
in smartphones, robots, and drones. Exploiting inertial data for accurate and
reliable navigation and localization has attracted significant research and
industrial interest, as IMU measurements are completely ego-centric and
generally environment agnostic. Recent studies have shown that the notorious
issue of drift can be significantly alleviated by using deep neural networks
(DNNs), e.g. IONet. However, the lack of sufficient labelled data for training
and testing various architectures limits the proliferation of adopting DNNs in
IMU-based tasks. In this paper, we propose and release the Oxford Inertial
Odometry Dataset (OxIOD), a first-of-its-kind data collection for
inertial-odometry research, with all sequences having ground-truth labels. Our
dataset contains 158 sequences totalling more than 42 km in total distance,
much larger than previous inertial datasets. Another notable feature of this
dataset lies in its diversity, which can reflect the complex motions of
phone-based IMUs in various everyday usage. The measurements were collected
with four different attachments (handheld, in the pocket, in the handbag and on
the trolley), four motion modes (halting, walking slowly, walking normally, and
running), five different users, four types of off-the-shelf consumer phones,
and large-scale localization from office buildings. Deep inertial tracking
experiments were conducted to show the effectiveness of our dataset in training
deep neural network models and evaluate learning-based and model-based
algorithms. The OxIOD Dataset is available at: http://deepio.cs.ox.ac.uk
A Quantitative Evaluation of Natural Language Question Interpretation for Question Answering Systems
Authors: Takuto Asakura, Jin-Dong Kim, Yasunori Yamamoto, Yuka Tateisi, Toshihisa Takagi
Date: 2018-09-20 00:00:00
Tasks: Question Answering

Systematic benchmark evaluation plays an important role in the process of
improving technologies for Question Answering (QA) systems. While currently
there are a number of existing evaluation methods for natural language (NL) QA
systems, most of them consider only the final answers, limiting their utility
within a black box style evaluation. Herein, we propose a subdivided evaluation
approach to enable finer-grained evaluation of QA systems, and present an
evaluation tool which targets the NL question (NLQ) interpretation step, an
initial step of a QA pipeline. The results of experiments using two public
benchmark datasets suggest that we can get a deeper insight about the
performance of a QA system using the proposed approach, which should provide a
better guidance for improving the systems, than using black box style
approaches.
Building Context-aware Clause Representations for Situation Entity Type Classification
Authors: Zeyu Dai, Ruihong Huang
Date: 2018-09-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Vocal Bursts Type Prediction

Capabilities to categorize a clause based on the type of situation entity
(e.g., events, states and generic statements) the clause introduces to the
discourse can benefit many NLP applications. Observing that the situation
entity type of a clause depends on discourse functions the clause plays in a
paragraph and the interpretation of discourse functions depends heavily on
paragraph-wide contexts, we propose to build context-aware clause
representations for predicting situation entity types of clauses. Specifically,
we propose a hierarchical recurrent neural network model to read a whole
paragraph at a time and jointly learn representations for all the clauses in
the paragraph by extensively modeling context influences and inter-dependencies
of clauses. Experimental results show that our model achieves the
state-of-the-art performance for clause-level situation entity classification
on the genre-rich MASC+Wiki corpus, which approaches human-level performance.
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Authors: Vibhavari Dasagi, Robert Lee, Serena Mou, Jake Bruce, Niko Sünderhauf, Jürgen Leitner
Date: 2018-09-20 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, object-detection, Object Detection, Reinforcement Learning, Reinforcement Learning (RL), Robot Manipulation, Transfer Learning

Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge. This results in prohibitively long training times for use on real-world robotic tasks. Existing algorithms capable of extracting task-level representations from high-dimensional inputs, e.g. object detection, often produce outputs of varying lengths, restricting their use in RL methods due to the need for neural networks to have fixed length inputs. In this work, we propose a framework that combines deep sets encoding, which allows for variable-length abstract representations, with modular RL that utilizes these representations, decoupling high-level decision making from low-level control. We successfully demonstrate our approach on the robot manipulation task of object sorting, showing that this method can learn effective policies within mere minutes of highly simplified simulation. The learned policies can be directly deployed on a robot without further training, and generalize to variations of the task unseen during training.
Towards Discrete Solution: A Sparse Preserving Method for Correspondence Problem
Authors: Bo Jiang
Date: 2018-09-20 00:00:00

Many problems of interest in computer vision can be formulated as a problem
of finding consistent correspondences between two feature sets. Feature
correspondence (matching) problem with one-to-one mapping constraint is usually
formulated as an Integral Quadratic Programming (IQP) problem with permutation
(or orthogonal) constraint. Since it is NP-hard, relaxation models are
required. One main challenge for optimizing IQP matching problem is how to
incorporate the discrete one-to-one mapping (permutation) constraint in its
quadratic objective optimization. In this paper, we present a new relaxation
model, called Sparse Constraint Preserving Matching (SPM), for IQP matching
problem. SPM is motivated by our observation that the discrete permutation
constraint can be well encoded via a sparse constraint. Comparing with
traditional relaxation models, SPM can incorporate the discrete one-to-one
mapping constraint straightly via a sparse constraint and thus provides a
tighter relaxation for original IQP matching problem. A simple yet effective
update algorithm has been derived to solve the proposed SPM model. Experimental
results on several feature matching tasks demonstrate the effectiveness and
efficiency of SPM method.