pwc-archive/papers-with-abstracts huggingface dataset:
Controlling Robot Morphology from Incomplete Measurements
Authors: Martin Pecka, Karel Zimmermann, Michal Reinštein, Tomáš Svoboda
Date: 2016-12-08 00:00:00

Mobile robots with complex morphology are essential for traversing rough
terrains in Urban Search & Rescue missions (USAR). Since teleoperation of the
complex morphology causes high cognitive load of the operator, the morphology
is controlled autonomously. The autonomous control measures the robot state and
surrounding terrain which is usually only partially observable, and thus the
data are often incomplete. We marginalize the control over the missing
measurements and evaluate an explicit safety condition. If the safety condition
is violated, tactile terrain exploration by the body-mounted robotic arm
gathers the missing data.
Recovery guarantee of weighted low-rank approximation via alternating minimization
Authors: Yuanzhi Li, YIngyu Liang, Andrej Risteski
Date: 2016-02-06 00:00:00
Tasks: Matrix Completion

Many applications require recovering a ground truth low-rank matrix from
noisy observations of the entries, which in practice is typically formulated as
a weighted low-rank approximation problem and solved by non-convex optimization
heuristics such as alternating minimization. In this paper, we provide provable
recovery guarantee of weighted low-rank via a simple alternating minimization
algorithm. In particular, for a natural class of matrices and weights and
without any assumption on the noise, we bound the spectral norm of the
difference between the recovered matrix and the ground truth, by the spectral
norm of the weighted noise plus an additive error that decreases exponentially
with the number of rounds of alternating minimization, from either
initialization by SVD or, more importantly, random initialization. These
provide the first theoretical results for weighted low-rank via alternating
minimization with non-binary deterministic weights, significantly generalizing
those for matrix completion, the special case with binary weights, since our
assumptions are similar or weaker than those made in existing works.
Furthermore, this is achieved by a very simple algorithm that improves the
vanilla alternating minimization with a simple clipping step.
  The key technical challenge is that under non-binary deterministic weights,
na\"ive alternating steps will destroy the incoherence and spectral properties
of the intermediate solutions, which are needed for making progress towards the
ground truth. We show that the properties only need to hold in an average sense
and can be achieved by the clipping step.
  We further provide an alternating algorithm that uses a whitening step that
keeps the properties via SDP and Rademacher rounding and thus requires weaker
assumptions. This technique can potentially be applied in some other
applications and is of independent interest.
Predicting Ground-Level Scene Layout from Aerial Imagery
Authors: Menghua Zhai, Zachary Bessinger, Scott Workman, Nathan Jacobs
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Cross-View Image-to-Image Translation, Segmentation, Semantic Segmentation

We introduce a novel strategy for learning to extract semantically meaningful
features from aerial imagery. Instead of manually labeling the aerial imagery,
we propose to predict (noisy) semantic features automatically extracted from
co-located ground imagery. Our network architecture takes an aerial image as
input, extracts features using a convolutional neural network, and then applies
an adaptive transformation to map these features into the ground-level
perspective. We use an end-to-end learning approach to minimize the difference
between the semantic segmentation extracted directly from the ground image and
the semantic segmentation predicted solely based on the aerial image. We show
that a model learned using this strategy, with no additional training, is
already capable of rough semantic labeling of aerial imagery. Furthermore, we
demonstrate that by finetuning this model we can achieve more accurate semantic
segmentation than two baseline initialization strategies. We use our network to
address the task of estimating the geolocation and geoorientation of a ground
image. Finally, we show how features extracted from an aerial image can be used
to hallucinate a plausible ground-level panorama.
A note on the triangle inequality for the Jaccard distance
Authors: Sven Kosub
Date: 2016-12-08 00:00:00

Two simple proofs of the triangle inequality for the Jaccard distance in
terms of nonnegative, monotone, submodular functions are given and discussed.
Towards better decoding and language model integration in sequence to sequence models
Authors: Jan Chorowski, Navdeep Jaitly
Date: 2016-12-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

The recently proposed Sequence-to-Sequence (seq2seq) framework advocates
replacing complex data processing pipelines, such as an entire automatic speech
recognition system, with a single neural network trained in an end-to-end
fashion. In this contribution, we analyse an attention-based seq2seq speech
recognition system that directly transcribes recordings into characters. We
observe two shortcomings: overconfidence in its predictions and a tendency to
produce incomplete transcriptions when language models are used. We propose
practical solutions to both problems achieving competitive speaker independent
word error rates on the Wall Street Journal dataset: without separate language
models we reach 10.6% WER, while together with a trigram language model, we
reach 6.7% WER.
Domain knowledge assisted cyst segmentation in OCT retinal images
Authors: Karthik Gopinath, Jayanthi Sivaswamy
Date: 2016-12-08 00:00:00
Tasks: Ensemble Learning

3D imaging modalities are becoming increasingly popular and relevant in
retinal imaging owing to their effectiveness in highlighting structures in
sub-retinal layers. OCT is one such modality which has great importance in the
context of analysis of cystoid structures in subretinal layers. Signal to noise
ratio(SNR) of the images obtained from OCT is less and hence automated and
accurate determination of cystoid structures from OCT is a challenging task. We
propose an automated method for detecting/segmenting cysts in 3D OCT volumes.
The proposed method is biologically inspired and fast aided by the domain
knowledge about the cystoid structures. An ensemble learning methodRandom
forests is learnt for classification of detected region into cyst region. The
method achieves detection and segmentation in a unified setting. We believe the
proposed approach with further improvements can be a promising starting point
for more robust approach. This method is validated against the training set
achieves a mean dice coefficient of 0.3893 with a standard deviation of 0.2987
A fuzzy approach for segmentation of touching characters
Authors: Giuseppe Airò Farulla, Nadir Murru, Rosaria Rossini
Date: 2016-12-08 00:00:00

The problem of correctly segmenting touching characters is an hard task to
solve and it is of major relevance in pattern recognition. In the recent years,
many methods and algorithms have been proposed; still, a definitive solution is
far from being found. In this paper, we propose a novel method based on fuzzy
logic. The proposed method combines in a novel way three features for
segmenting touching characters that have been already proposed in other studies
but have been exploited only singularly so far. The proposed strategy is based
on a 3--input/1--output fuzzy inference system with fuzzy rules specifically
optimized for segmenting touching characters in the case of Latin printed and
handwritten characters. The system performances are illustrated and supported
by numerical examples showing that our approach can achieve a reasonable good
overall accuracy in segmenting characters even on tricky conditions of touching
characters. Moreover, numerical results suggest that the method can be applied
to many different datasets of characters by means of a convenient tuning of the
fuzzy sets and rules.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Authors: Judy Hoffman, Dequan Wang, Fisher Yu, Trevor Darrell
Date: 2016-12-08 00:00:00
Tasks: Image-to-Image Translation, Semantic Segmentation, Synthetic-to-Real Translation

Fully convolutional models for dense prediction have proven successful for a
wide range of visual tasks. Such models perform well in a supervised setting,
but performance can be surprisingly poor under domain shifts that appear mild
to a human observer. For example, training on one city and testing on another
in a different geographic region and/or weather condition may result in
significantly degraded performance due to pixel-level distribution shift. In
this paper, we introduce the first domain adaptive semantic segmentation
method, proposing an unsupervised adversarial approach to pixel prediction
problems. Our method consists of both global and category specific adaptation
techniques. Global domain alignment is performed using a novel semantic
segmentation network with fully convolutional domain adversarial learning. This
initially adapted space then enables category specific adaptation through a
generalization of constrained weak learning, with explicit transfer of the
spatial layout from the source to the target domains. Our approach outperforms
baselines across different settings on multiple large-scale datasets, including
adapting across various real city environments, different synthetic
sub-domains, from simulated to real environments, and on a novel large-scale
dash-cam dataset.
Learning Video Object Segmentation from Static Images
Authors: Anna Khoreva, Federico Perazzi, Rodrigo Benenson, Bernt Schiele, Alexander Sorkine-Hornung
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Instance Segmentation, Object, Object Tracking, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

Inspired by recent advances of deep learning in instance segmentation and
object tracking, we introduce video object segmentation problem as a concept of
guided instance segmentation. Our model proceeds on a per-frame basis, guided
by the output of the previous frame towards the object of interest in the next
frame. We demonstrate that highly accurate object segmentation in videos can be
enabled by using a convnet trained with static images only. The key ingredient
of our approach is a combination of offline and online learning strategies,
where the former serves to produce a refined mask from the previous frame
estimate and the latter allows to capture the appearance of the specific object
instance. Our method can handle different types of input annotations: bounding
boxes and segments, as well as incorporate multiple annotated frames, making
the system suitable for diverse applications. We obtain competitive results on
three different datasets, independently from the type of input annotation.
Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm
Authors: Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse, Josiane Zerubia
Date: 2016-12-08 00:00:00
Tasks: regression

We propose a novel tree-like curvilinear structure reconstruction algorithm
based on supervised learning and graph theory. In this work we analyze image
patches to obtain the local major orientations and the rankings that correspond
to the curvilinear structure. To extract local curvilinear features, we compute
oriented gradient information using steerable filters. We then employ
Structured Support Vector Machine for ordinal regression of the input image
patches, where the ordering is determined by shape similarity to latent
curvilinear structure. Finally, we progressively reconstruct the curvilinear
structure by looking for geodesic paths connecting remote vertices in the graph
built on the structured output rankings. Experimental results show that the
proposed algorithm faithfully provides topological features of the curvilinear
structures using minimal pixels for various datasets.
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Authors: Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
Date: 2016-12-08 00:00:00
Tasks: Denoising

We focus on the maximum regularization parameter for anisotropic
total-variation denoising. It corresponds to the minimum value of the
regularization parameter above which the solution remains constant. While this
value is well know for the Lasso, such a critical value has not been
investigated in details for the total-variation. Though, it is of importance
when tuning the regularization parameter as it allows fixing an upper-bound on
the grid for which the optimal parameter is sought. We establish a closed form
expression for the one-dimensional case, as well as an upper-bound for the
two-dimensional case, that appears reasonably tight in practice. This problem
is directly linked to the computation of the pseudo-inverse of the divergence,
which can be quickly obtained by performing convolutions in the Fourier domain.
Towards Information-Seeking Agents
Authors: Philip Bachman, Alessandro Sordoni, Adam Trischler
Date: 2016-12-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We develop a general problem setting for training and testing the ability of
agents to gather information efficiently. Specifically, we present a collection
of tasks in which success requires searching through a partially-observed
environment, for fragments of information which can be pieced together to
accomplish various goals. We combine deep architectures with techniques from
reinforcement learning to develop agents that solve our tasks. We shape the
behavior of these agents by combining extrinsic and intrinsic rewards. We
empirically demonstrate that these agents learn to search actively and
intelligently for new information to reduce their uncertainty, and to exploit
information they have already acquired.
An Architecture for Deep, Hierarchical Generative Models
Authors: Philip Bachman
Date: 2016-12-08 00:00:00
Proceeding: NeurIPS 2016 12

We present an architecture which lets us train deep, directed generative
models with many layers of latent variables. We include deterministic paths
between all latent variables and the generated output, and provide a richer set
of connections between computations for inference and generation, which enables
more effective communication of information throughout the model during
training. To improve performance on natural images, we incorporate a
lightweight autoregressive model in the reconstruction distribution. These
techniques permit end-to-end training of models with 10+ layers of latent
variables. Experiments show that our approach achieves state-of-the-art
performance on standard image modelling benchmarks, can expose latent class
structure in the absence of label information, and can provide convincing
imputations of occluded regions in natural images.
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
Authors: Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner, Andreas Christe, Stavroula Mougiakakou
Date: 2016-12-08 00:00:00
Tasks: Medical Image Analysis, Texture Classification, Transfer Learning

Early diagnosis of interstitial lung diseases is crucial for their treatment,
but even experienced physicians find it difficult, as their clinical
manifestations are similar. In order to assist with the diagnosis,
computer-aided diagnosis (CAD) systems have been developed. These commonly rely
on a fixed scale classifier that scans CT images, recognizes textural lung
patterns and generates a map of pathologies. In a previous study, we proposed a
method for classifying lung tissue patterns using a deep convolutional neural
network (CNN), with an architecture designed for the specific problem. In this
study, we present an improved method for training the proposed network by
transferring knowledge from the similar domain of general texture
classification. Six publicly available texture databases are used to pretrain
networks with the proposed architecture, which are then fine-tuned on the lung
tissue data. The resulting CNNs are combined in an ensemble and their fused
knowledge is compressed back to a network with the original architecture. The
proposed approach resulted in an absolute increase of about 2% in the
performance of the proposed CNN. The results demonstrate the potential of
transfer learning in the field of medical image analysis, indicate the textural
nature of the problem and show that the method used for training a network can
be as important as designing its architecture.
Inverses, Conditionals and Compositional Operators in Separative Valuation Algebra
Authors: Juerg Kohlas
Date: 2016-12-08 00:00:00

Compositional models were introduce by Jirousek and Shenoy in the general
framework of valuation-based systems. They based their theory on an axiomatic
system of valuations involving not only the operations of combination and
marginalisation, but also of removal. They claimed that this systems covers
besides the classical case of discrete probability distributions, also the
cases of Gaussian densities and belief functions, and many other systems.
  Whereas their results on the compositional operator are correct, the
axiomatic basis is not sufficient to cover the examples claimed above. We
propose here a different axiomatic system of valuation algebras, which permits
a rigorous mathematical theory of compositional operators in valuation-based
systems and covers all the examples mentioned above. It extends the classical
theory of inverses in semigroup theory and places thereby the present theory
into its proper mathematical frame. Also this theory sheds light on the
different structures of valuation-based systems, like regular algebras
(represented by probability potentials), canncellative algebras (Gaussian
potentials) and general separative algebras (density functions).
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Authors: Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7

Removing pixel-wise heterogeneous motion blur is challenging due to the
ill-posed nature of the problem. The predominant solution is to estimate the
blur kernel by adding a prior, but the extensive literature on the subject
indicates the difficulty in identifying a prior which is suitably informative,
and general. Rather than imposing a prior based on theory, we propose instead
to learn one from the data. Learning a prior over the latent image would
require modeling all possible image content. The critical observation
underpinning our approach is thus that learning the motion flow instead allows
the model to focus on the cause of the blur, irrespective of the image content.
This is a much easier learning task, but it also avoids the iterative process
through which latent image priors are typically applied. Our approach directly
estimates the motion flow from the blurred image through a fully-convolutional
deep neural network (FCN) and recovers the unblurred image from the estimated
motion flow. Our FCN is the first universal end-to-end mapping from the blurred
image to the dense motion flow. To train the FCN, we simulate motion flows to
generate synthetic blurred-image-motion-flow pairs thus avoiding the need for
human labeling. Extensive experiments on challenging realistic blurred images
demonstrate that the proposed method outperforms the state-of-the-art.
Imaging around corners with single-pixel detector by computational ghost imaging
Authors: Bin Bai, Jianbin Liu, Yu Zhou, Songlin Zhang, Yuchen He, Zhuo Xu
Date: 2016-12-08 00:00:00
Tasks: Object

We have designed a single-pixel camera with imaging around corners based on
computational ghost imaging. It can obtain the image of an object when the
camera cannot look at the object directly. Our imaging system explores the fact
that a bucket detector in a ghost imaging setup has no spatial resolution
capability. A series of experiments have been designed to confirm our
predictions. This camera has potential applications for imaging around corner
or other similar environments where the object cannot be observed directly.
Filter sharing: Efficient learning of parameters for volumetric convolutions
Authors: Rahul Venkataramani, Sheshadri Thiruvenkadam, Prasad Sudhakar, Hariharan Ravishankar, Vivek Vaidya
Date: 2016-12-08 00:00:00
Tasks: Lung Nodule Segmentation

Typical convolutional neural networks (CNNs) have several millions of
parameters and require a large amount of annotated data to train them. In
medical applications where training data is hard to come by, these
sophisticated machine learning models are difficult to train. In this paper, we
propose a method to reduce the inherent complexity of CNNs during training by
exploiting the significant redundancy that is noticed in the learnt CNN
filters. Our method relies on finding a small set of filters and mixing
coefficients to derive every filter in each convolutional layer at the time of
training itself, thereby reducing the number of parameters to be trained. We
consider the problem of 3D lung nodule segmentation in CT images and
demonstrate the effectiveness of our method in achieving good results with only
few training examples.
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Authors: James H. Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis, Matthan WA Caan, Claire Steves, Tim D Spector, Giovanni Montana
Date: 2016-12-08 00:00:00
Tasks: GPR, valid

Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people and deviations from healthy brain ageing
have been associated with cognitive impairment and disease. Here we sought to
further establish the credentials of "brain-predicted age" as a biomarker of
individual differences in the brain ageing process, using a predictive
modelling approach based on deep learning, and specifically convolutional
neural networks (CNN), and applied to both pre-processed and raw T1-weighted
MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted
age using a large dataset of healthy adults (N = 2001). Next, we sought to
establish the heritability of brain-predicted age using a sample of monozygotic
and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and
multi-centre reliability of brain-predicted age using two samples
(within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were
generated and compared to a Gaussian Process Regression (GPR) approach, on all
datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps
generated by Statistical Parametric Mapping (SPM) or raw data. Brain-predicted
age represents an accurate, highly reliable and genetically-valid phenotype,
that has potential to be used as a biomarker of brain ageing. Moreover, age
predictions can be accurately generated on raw T1-MRI data, substantially
reducing computation time for novel data, bringing the process closer to giving
real-time information on brain health in clinical settings.