pwc-archive/papers-with-abstracts huggingface dataset:
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Authors: Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, Bennett A. Landman
Date: 2018-10-15 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available (https://github.com/MASILab/SynSeg-Net).
Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network
Authors: Mohbat Tharani, Numan Khurshid, Murtaza Taj
Date: 2018-10-15 00:00:00
Tasks: Decoder, Image Retrieval, Retrieval

The advent of deep perceptual networks brought about a paradigm shift in
machine vision and image perception. Image apprehension lately carried out by
hand-crafted features in the latent space have been replaced by deep features
acquired from supervised networks for improved understanding. However, such
deep networks require strict supervision with a substantial amount of the
labeled data for authentic training process. These methods perform poorly in
domains lacking labeled data especially in case of remote sensing image
retrieval. Resolving this, we propose an unsupervised encoder-decoder feature
for remote sensing image matching (RSIM). Moreover, we replace the conventional
distance metrics with a deep discriminator network to identify the similarity
of the image pairs. To the best of our knowledge, discriminator network has
never been used before for solving RSIM problem. Results have been validated
with two publicly available benchmark remote sensing image datasets. The
technique has also been investigated for content-based remote sensing image
retrieval (CBRSIR); one of the widely used applications of RSIM. Results
demonstrate that our technique supersedes the state-of-the-art methods used for
unsupervised image matching with mean average precision (mAP) of 81%, and image
retrieval with an overall improvement in mAP score of about 12%.
CSV: Image Quality Assessment Based on Color, Structure, and Visual System
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-10-15 00:00:00
Tasks: Image Quality Assessment

This paper presents a full-reference image quality estimator based on color,
structure, and visual system characteristics denoted as CSV. In contrast to the
majority of existing methods, we quantify perceptual color degradations rather
than absolute pixel-wise changes. We use the CIEDE2000 color difference
formulation to quantify low-level color degradations and the Earth Mover's
Distance between color name descriptors to measure significant color
degradations. In addition to the perceptual color difference, CSV also contains
structural and perceptual differences. Structural feature maps are obtained by
mean subtraction and divisive normalization, and perceptual feature maps are
obtained from contrast sensitivity formulations of retinal ganglion cells. The
proposed quality estimator CSV is tested on the LIVE, the Multiply Distorted
LIVE, and the TID 2013 databases, and it is always among the top two performing
quality estimators in terms of at least ranking, monotonic behavior or
linearity.
Refacing: reconstructing anonymized facial features using GANs
Authors: David Abramian, Anders Eklund
Date: 2018-10-15 00:00:00
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Anonymization of medical images is necessary for protecting the identity of
the test subjects, and is therefore an essential step in data sharing. However,
recent developments in deep learning may raise the bar on the amount of
distortion that needs to be applied to guarantee anonymity. To test such
possibilities, we have applied the novel CycleGAN unsupervised image-to-image
translation framework on sagittal slices of T1 MR images, in order to
reconstruct facial features from anonymized data. We applied the CycleGAN
framework on both face-blurred and face-removed images. Our results show that
face blurring may not provide adequate protection against malicious attempts at
identifying the subjects, while face removal provides more robust
anonymization, but is still partially reversible.
Channel Splitting Network for Single MR Image Super-Resolution
Authors: Xiaole Zhao, Yulun Zhang, Tao Zhang, Xueming Zou
Date: 2018-10-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

High resolution magnetic resonance (MR) imaging is desirable in many clinical applications due to its contribution to more accurate subsequent analyses and early clinical diagnoses. Single image super resolution (SISR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. In the past few years, SISR methods based on deep learning techniques, especially convolutional neural networks (CNNs), have achieved state-of-the-art performance on natural images. However, the information is gradually weakened and training becomes increasingly difficult as the network deepens. The problem is more serious for medical images because lacking high quality and effective training samples makes deep models prone to underfitting or overfitting. Nevertheless, many current models treat the hierarchical features on different channels equivalently, which is not helpful for the models to deal with the hierarchical features discriminatively and targetedly. To this end, we present a novel channel splitting network (CSN) to ease the representational burden of deep models. The proposed CSN model divides the hierarchical features into two branches, i.e., residual branch and dense branch, with different information transmissions. The residual branch is able to promote feature reuse, while the dense branch is beneficial to the exploration of new features. Besides, we also adopt the merge-and-run mapping to facilitate information integration between different branches. Extensive experiments on various MR images, including proton density (PD), T1 and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.
Hedging Algorithms and Repeated Matrix Games
Authors: Bruno Bouzy, Marc Métivier, Damien Pellier
Date: 2018-10-15 00:00:00

Playing repeated matrix games (RMG) while maximizing the cumulative returns
is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous
work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours
on average in RMG. Besides, hedging algorithms have been shown to be effective
on prediction problems. An hedging algorithm is made up with a top-level
algorithm and a set of basic algorithms. To make its decision, an hedging
algorithm uses its top-level algorithm to choose a basic algorithm, and the
chosen algorithm makes the decision. This paper experimentally shows that
well-selected hedging algorithms are better on average than all previous MAL
algorithms on the task of playing RMG against various players. $S$ is a very
good top-level algorithm, and $UCB$ and $M3$ are very good basic algorithms.
Furthermore, two-level hedging algorithms are more effective than one-level
hedging algorithms, and three levels are not better than two levels.
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Authors: Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman
Date: 2018-10-15 00:00:00
Tasks: Virtual Staining

Histopathological evaluation of tissue samples is a key practice in patient
diagnosis and drug development, especially in oncology. Historically,
Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard
staining. However, in many cases, various target specific stains, including
immunohistochemistry (IHC), are needed in order to highlight specific
structures in the tissue. As tissue is scarce and staining procedures are
tedious, it would be beneficial to generate images of stained tissue virtually.
Virtual staining could also generate in-silico multiplexing of different stains
on the same tissue segment. In this paper, we present a sample application that
generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an
unsupervised deep learning approach based on CycleGAN. We also propose a method
to deal with tiling artifacts caused by normalization layers and we validate
our approach by comparing the results of tissue analysis algorithms for virtual
and real images.
Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots
Authors: Hafez Farazi, Sven Behnke
Date: 2018-10-15 00:00:00
Tasks: Real-Time Visual Tracking, Visual Tracking

The use of a team of humanoid robots to collaborate in completing a task is
an increasingly important field of research. One of the challenges in achieving
collaboration, is mutual identification and tracking of the robots. This work
presents a real-time vision-based approach to the detection and tracking of
robots of known appearance, based on the images captured by a stationary robot.
A Histogram of Oriented Gradients descriptor is used to detect the robots and
the robot headings are estimated by a multiclass classifier. The tracked robots
report their own heading estimate from magnetometer readings. For tracking, a
cost function based on position and heading is applied to each of the
tracklets, and a globally optimal labeling of the detected robots is found
using the Hungarian algorithm. The complete identification and tracking system
was tested using two igus Humanoid Open Platform robots on a soccer field. We
expect that a similar system can be used with other humanoid robots, such as
Nao and DARwIn-OP
A Priori Estimates of the Population Risk for Two-layer Neural Networks
Authors: Weinan E, Chao Ma, Lei Wu
Date: 2018-10-15 00:00:00
Proceeding: ICLR 2019

New estimates for the population risk are established for two-layer neural networks. These estimates are nearly optimal in the sense that the error rates scale in the same way as the Monte Carlo error rates. They are equally effective in the over-parametrized regime when the network size is much larger than the size of the dataset. These new estimates are a priori in nature in the sense that the bounds depend only on some norms of the underlying functions to be fitted, not the parameters in the model, in contrast with most existing results which are a posteriori in nature. Using these a priori estimates, we provide a perspective for understanding why two-layer neural networks perform better than the related kernel methods.
Unsupervised Ensemble Learning via Ising Model Approximation with Application to Phenotyping Prediction
Authors: Luwan Zhang, Tianrun Cai
Date: 2018-10-15 00:00:00
Tasks: Ensemble Learning

Unsupervised ensemble learning has long been an interesting yet challenging
problem that comes to prominence in recent years with the increasing demand of
crowdsourcing in various applications. In this paper, we propose a novel
method-- unsupervised ensemble learning via Ising model approximation (unElisa)
that combines a pruning step with a predicting step. We focus on the binary
case and use an Ising model to characterize interactions between the ensemble
and the underlying true classifier. The presence of an edge between an observed
classifier and the true classifier indicates a direct dependence whereas the
absence indicates the corresponding one provides no additional information and
shall be eliminated. This observation leads to the pruning step where the key
is to recover the neighborhood of the true classifier. We show that it can be
recovered successfully with exponentially decaying error in the
high-dimensional setting by performing nodewise $\ell_1$-regularized logistic
regression. The pruned ensemble allows us to get a consistent estimate of the
Bayes classifier for predicting. We also propose an augmented version of
majority voting by reversing all labels given by a subgroup of the pruned
ensemble. We demonstrate the efficacy of our method through extensive numerical
experiments and through the application to EHR-based phenotyping prediction on
Rheumatoid Arthritis (RA) using data from Partners Healthcare System.
Neural Adaptation Layers for Cross-domain Named Entity Recognition
Authors: Bill Yuchen Lin, Wei Lu
Date: 2018-10-15 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Cross-Domain Named Entity Recognition, Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recent research efforts have shown that neural architectures can be effective
in conventional information extraction tasks such as named entity recognition,
yielding state-of-the-art results on standard newswire datasets. However,
despite significant resources required for training such models, the
performance of a model trained on one domain typically degrades dramatically
when applied to a different domain, yet extracting entities from new emerging
domains such as social media can be of significant interest. In this paper, we
empirically investigate effective methods for conveniently adapting an
existing, well-trained neural NER model for a new domain. Unlike existing
approaches, we propose lightweight yet effective methods for performing domain
adaptation for neural models. Specifically, we introduce adaptation layers on
top of existing neural architectures, where no re-training using the source
domain data is required. We conduct extensive empirical studies and show that
our approach significantly outperforms state-of-the-art methods.
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation
Authors: Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa
Date: 2018-10-15 00:00:00
Tasks: Machine Translation, Sentence, Translation

Universal language representation is the holy grail in machine translation
(MT). Thanks to the new neural MT approach, it seems that there are good
perspectives towards this goal. In this paper, we propose a new architecture
based on combining variational autoencoders with encoder-decoders and
introducing an interlingual loss as an additional training objective. By adding
and forcing this interlingual loss, we are able to train multiple encoders and
decoders for each language, sharing a common universal representation. Since
the final objective of this universal representation is producing close results
for similar input sentences (in any language), we propose to evaluate it by
encoding the same sentence in two different languages, decoding both latent
representations into the same language and comparing both outputs. Preliminary
results on the WMT 2017 Turkish/English task shows that the proposed
architecture is capable of learning a universal language representation and
simultaneously training both translation directions with state-of-the-art
results.
Deep Reinforcement Learning
Authors: Yuxi Li
Date: 2018-10-15 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We discuss deep reinforcement learning in an overview style. We draw a big
picture, filled with details. We discuss six core elements, six important
mechanisms, and twelve applications, focusing on contemporary work, and in
historical contexts. We start with background of artificial intelligence,
machine learning, deep learning, and reinforcement learning (RL), with
resources. Next we discuss RL core elements, including value function, policy,
reward, model, exploration vs. exploitation, and representation. Then we
discuss important mechanisms for RL, including attention and memory,
unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and
learning to learn. After that, we discuss RL applications, including games,
robotics, natural language processing (NLP), computer vision, finance, business
management, healthcare, education, energy, transportation, computer systems,
and, science, engineering, and art. Finally we summarize briefly, discuss
challenges and opportunities, and close with an epilogue.
Towards Providing Explanations for AI Planner Decisions
Authors: Rita Borgo, Michael Cashmore, Daniele Magazzeni
Date: 2018-10-15 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

In order to engender trust in AI, humans must understand what an AI system is
trying to achieve, and why. To overcome this problem, the underlying AI process
must produce justifications and explanations that are both transparent and
comprehensible to the user. AI Planning is well placed to be able to address
this challenge. In this paper we present a methodology to provide initial
explanations for the decisions made by the planner. Explanations are created by
allowing the user to suggest alternative actions in plans and then compare the
resulting plans with the one found by the planner. The methodology is
implemented in the new XAI-Plan framework.
Deep Photovoltaic Nowcasting
Authors: Jinsong Zhang, Rodrigo Verschae, Shohei Nobuhara, Jean-François Lalonde
Date: 2018-10-15 00:00:00
Tasks: Management

Predicting the short-term power output of a photovoltaic panel is an
important task for the efficient management of smart grids. Short-term
forecasting at the minute scale, also known as nowcasting, can benefit from sky
images captured by regular cameras and installed close to the solar panel.
However, estimating the weather conditions from these images---sun intensity,
cloud appearance and movement, etc.---is a very challenging task that the
community has yet to solve with traditional computer vision techniques. In this
work, we propose to learn the relationship between sky appearance and the
future photovoltaic power output using deep learning. We train several variants
of convolutional neural networks which take historical photovoltaic power
values and sky images as input and estimate photovoltaic power in a very short
term future. In particular, we compare three different architectures based on:
a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a
long short term memory (LSTM) module. We evaluate our approach quantitatively
on a dataset of photovoltaic power values and corresponding images gathered in
Kyoto, Japan. Our experiments reveal that the MLP network, already used
similarly in previous work, achieves an RMSE skill score of 7% over the
commonly-used persistence baseline on the 1-minute future photovoltaic power
prediction task. Our CNN-based network improves upon this with a 12% skill
score. In contrast, our LSTM-based model, which can learn the temporal
dependencies in the data, achieves a 21% RMSE skill score, thus outperforming
all other approaches.
Polyphonic Sound Event Detection by using Capsule Neural Network
Authors: Fabio Vesperini, Leonardo Gabrielli, Emanuele Principi, Stefano Squartini
Date: 2018-10-15 00:00:00
Tasks: Event Detection, Sound Event Detection

Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. %
environment. Nowadays, Deep Learning offers valuable techniques for this goal
such as Convolutional Neural Networks (CNNs). The Capsule Neural Network
(CapsNet) architecture has been recently introduced in the image processing
field with the intent to overcome some of the known limitations of CNNs,
specifically regarding the scarce robustness to affine transformations (i.e.,
perspective, size, orientation) and the detection of overlapped images. This
motivated the authors to employ CapsNets to deal with the polyphonic-SED task,
in which multiple sound events occur simultaneously. Specifically, we propose
to exploit the capsule units to represent a set of distinctive properties for
each individual sound event. Capsule units are connected through a so-called
\textit{dynamic routing} that encourages learning part-whole relationships and
improves the detection performance in a polyphonic context. This paper reports
extensive evaluations carried out on three publicly available datasets, showing
how the CapsNet-based algorithm not only outperforms standard CNNs but also
allows to achieve the best results with respect to the state of the art
algorithms.
Compressively Sensed Image Recognition
Authors: Aysen Degerli, Sinem Aslan, Mehmet Yamac, Bulent Sankur, Moncef Gabbouj
Date: 2018-10-15 00:00:00
Tasks: Compressive Sensing, General Classification, image-classification, Image Classification

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.
Regret vs. Bandwidth Trade-off for Recommendation Systems
Authors: Linqi Song, Christina Fragouli, Devavrat Shah
Date: 2018-10-15 00:00:00
Tasks: Recommendation Systems

We consider recommendation systems that need to operate under wireless
bandwidth constraints, measured as number of broadcast transmissions, and
demonstrate a (tight for some instances) tradeoff between regret and bandwidth
for two scenarios: the case of multi-armed bandit with context, and the case
where there is a latent structure in the message space that we can exploit to
reduce the learning phase.
Improving Topic Models with Latent Feature Word Representations
Authors: Dat Quoc Nguyen, Richard Billingsley, Lan Du, Mark Johnson
Date: 2018-10-15 00:00:00
Proceeding: TACL 2015 1
Tasks: Clustering, Document Classification, General Classification, Topic Models

Probabilistic topic models are widely used to discover latent topics in
document collections, while latent feature vector representations of words have
been used to obtain high performance in many NLP tasks. In this paper, we
extend two different Dirichlet multinomial topic models by incorporating latent
feature vector representations of words trained on very large corpora to
improve the word-topic mapping learnt on a smaller corpus. Experimental results
show that by using information from the external corpora, our new models
produce significant improvements on topic coherence, document clustering and
document classification tasks, especially on datasets with few or short
documents.