pwc-archive/papers-with-abstracts huggingface dataset:
Learned Spectral Super-Resolution
Authors: Silvano Galliani, Charis Lanaras, Dimitrios Marmanis, Emmanuel Baltsavias, Konrad Schindler
Date: 2017-03-28 00:00:00
Tasks: Spectral Super-Resolution, Super-Resolution

We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hyper-spectral) wave-length bands. Just like the spatial statistics of
natural images has rich structure, which one can exploit as prior to predict
high-frequency content from a low resolution image, the same is also true in
the spectral domain: the materials and lighting conditions of the observed
world induce structure in the spectrum of wavelengths observed at a given
pixel. Surprisingly, very little work exists that attempts to use this
diagnosis and achieve blind spectral super-resolution from single images. We
start from the conjecture that, just like in the spatial domain, we can learn
the statistics of natural image spectra, and with its help generate finely
resolved hyper-spectral images from RGB input. Technically, we follow the
current best practice and implement a convolutional neural network (CNN), which
is trained to carry out the end-to-end mapping from an entire RGB image to the
corresponding hyperspectral image of equal size. We demonstrate spectral
super-resolution both for conventional RGB images and for multi-spectral
satellite data, outperforming the state-of-the-art.
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms
Authors: Mansoureh Aghabeig, Andrzej Jaszkiewicz
Date: 2017-03-28 00:00:00
Tasks: Combinatorial Optimization, Evolutionary Algorithms

In this paper we systematically study the importance, i.e., the influence on
performance, of the main design elements that differentiate scalarizing
functions-based multiobjective evolutionary algorithms (MOEAs). This class of
MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective
Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very
successful in multiple computational experiments and practical applications.
The two algorithms share the same common structure and differ only in two main
aspects. Using three different multiobjective combinatorial optimization
problems, i.e., the multiobjective symmetric traveling salesperson problem, the
traveling salesperson problem with profits, and the multiobjective set covering
problem, we show that the main differentiating design element is the mechanism
for parent selection, while the selection of weight vectors, either random or
uniformly distributed, is practically negligible if the number of uniform
weight vectors is sufficiently large.
StyleBank: An Explicit Representation for Neural Image Style Transfer
Authors: Dongdong Chen, Lu Yuan, Jing Liao, Nenghai Yu, Gang Hua
Date: 2017-03-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Incremental Learning, Style Transfer

We propose StyleBank, which is composed of multiple convolution filter banks
and each filter bank explicitly represents one style, for neural image style
transfer. To transfer an image to a specific style, the corresponding filter
bank is operated on top of the intermediate feature embedding produced by a
single auto-encoder. The StyleBank and the auto-encoder are jointly learnt,
where the learning is conducted in such a way that the auto-encoder does not
encode any style information thanks to the flexibility introduced by the
explicit filter bank representation. It also enables us to conduct incremental
learning to add a new image style by learning a new filter bank while holding
the auto-encoder fixed. The explicit style representation along with the
flexible network design enables us to fuse styles at not only the image level,
but also the region level. Our method is the first style transfer network that
links back to traditional texton mapping methods, and hence provides new
understanding on neural style transfer. Our method is easy to train, runs in
real-time, and produces results that qualitatively better or at least
comparable to existing methods.
Coherent Online Video Style Transfer
Authors: Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, Gang Hua
Date: 2017-03-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Stylization, Style Transfer, Video Style Transfer

Training a feed-forward network for fast neural style transfer of images is
proven to be successful. However, the naive extension to process video frame by
frame is prone to producing flickering results. We propose the first end-to-end
network for online video style transfer, which generates temporally coherent
stylized video sequences in near real-time. Two key ideas include an efficient
network by incorporating short-term coherence, and propagating short-term
coherence to long-term, which ensures the consistency over larger period of
time. Our network can incorporate different image stylization networks. We show
that the proposed method clearly outperforms the per-frame baseline both
qualitatively and quantitatively. Moreover, it can achieve visually comparable
coherence to optimization-based video style transfer, but is three orders of
magnitudes faster in runtime.
A practical approach to dialogue response generation in closed domains
Authors: Yichao Lu, Phillip Keung, Shaonan Zhang, Jason Sun, Vikas Bhardwaj
Date: 2017-03-28 00:00:00
Tasks: Response Generation

We describe a prototype dialogue response generation model for the customer
service domain at Amazon. The model, which is trained in a weakly supervised
fashion, measures the similarity between customer questions and agent answers
using a dual encoder network, a Siamese-like neural network architecture.
Answer templates are extracted from embeddings derived from past agent answers,
without turn-by-turn annotations. Responses to customer inquiries are generated
by selecting the best template from the final set of templates. We show that,
in a closed domain like customer service, the selected templates cover $>$70\%
of past customer inquiries. Furthermore, the relevance of the model-selected
templates is significantly higher than templates selected by a standard tf-idf
baseline.
Evaluation of Classifiers for Image Segmentation: Applications for Eucalypt Forest Inventory
Authors: Rodrigo M. Ferreira, Ricardo M. Marcacini
Date: 2017-03-28 00:00:00
Tasks: Decision Making, Image Segmentation, Management, Semantic Segmentation

The task of counting eucalyptus trees from aerial images collected by
unmanned aerial vehicles (UAVs) has been frequently explored by techniques of
estimation of the basal area, i.e, by determining the expected number of trees
based on sampling techniques. An alternative is the use of machine learning to
identify patterns that represent a tree unit, and then search for the
occurrence of these patterns throughout the image. This strategy depends on a
supervised image segmentation step to define predefined interest regions. Thus,
it is possible to automate the counting of eucalyptus trees in these images,
thereby increasing the efficiency of the eucalyptus forest inventory
management. In this paper, we evaluated 20 different classifiers for the image
segmentation task. A real sample was used to analyze the counting trees task
considering a practical environment. The results show that it possible to
automate this task with 0.7% counting error, in particular, by using strategies
based on a combination of classifiers. Moreover, we present some performance
considerations about each classifier that can be useful as a basis for
decision-making in future tasks.
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
Authors: Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Jingsong Xu, Zhenmin Tang
Date: 2016-11-22 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, object-detection, Object Detection

Labelled image datasets have played a critical role in high-level image
understanding. However, the process of manual labelling is both time-consuming
and labor intensive. To reduce the cost of manual labelling, there has been
increased research interest in automatically constructing image datasets by
exploiting web images. Datasets constructed by existing methods tend to have a
weak domain adaptation ability, which is known as the "dataset bias problem".
To address this issue, we present a novel image dataset construction framework
that can be generalized well to unseen target domains. Specifically, the given
queries are first expanded by searching the Google Books Ngrams Corpus to
obtain a rich semantic description, from which the visually non-salient and
less relevant expansions are filtered out. By treating each selected expansion
as a "bag" and the retrieved images as "instances", image selection can be
formulated as a multi-instance learning problem with constrained positive bags.
We propose to solve the employed problems by the cutting-plane and
concave-convex procedure (CCCP) algorithm. By using this approach, images from
different distributions can be kept while noisy images are filtered out. To
verify the effectiveness of our proposed approach, we build an image dataset
with 20 categories. Extensive experiments on image classification,
cross-dataset generalization, diversity comparison and object detection
demonstrate the domain robustness of our dataset.
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
Authors: Qiangui Huang, Weiyue Wang, Kevin Zhou, Suya You, Ulrich Neumann
Date: 2016-11-22 00:00:00
Tasks: Scene Labeling

Recurrent neural network (RNN), as a powerful contextual dependency modeling
framework, has been widely applied to scene labeling problems. However, this
work shows that directly applying traditional RNN architectures, which unfolds
a 2D lattice grid into a sequence, is not sufficient to model structure
dependencies in images due to the "impact vanishing" problem. First, we give an
empirical analysis about the "impact vanishing" problem. Then, a new RNN unit
named Recurrent Neural Network with explicit long range conditioning (RNN-ELC)
is designed to alleviate this problem. A novel neural network architecture is
built for scene labeling tasks where one of the variants of the new RNN unit,
Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used
to model multi scale contextual dependencies in images. We validate the use of
GRU-ELC units with state-of-the-art performance on three standard scene
labeling datasets. Comprehensive experiments demonstrate that the new GRU-ELC
unit benefits scene labeling problem a lot as it can encode longer contextual
dependencies in images more effectively than traditional RNN units.
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?
Authors: Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf
Date: 2017-03-28 00:00:00
Tasks: Clickbait Detection, Word Embeddings

The use of alluring headlines (clickbait) to tempt the readers has become a
growing practice nowadays. For the sake of existence in the highly competitive
media industry, most of the on-line media including the mainstream ones, have
started following this practice. Although the wide-spread practice of clickbait
makes the reader's reliability on media vulnerable, a large scale analysis to
reveal this fact is still absent. In this paper, we analyze 1.67 million
Facebook posts created by 153 media organizations to understand the extent of
clickbait practice, its impact and user engagement by using our own developed
clickbait detection model. The model uses distributed sub-word embeddings
learned from a large corpus. The accuracy of the model is 98.3%. Powered with
this model, we further study the distribution of topics in clickbait and
non-clickbait contents.
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
Authors: Benjamin D. Horne, Sibel Adali
Date: 2017-03-28 00:00:00
Tasks: Articles

The problem of fake news has gained a lot of attention as it is claimed to
have had a significant impact on 2016 US Presidential Elections. Fake news is
not a new problem and its spread in social networks is well-studied. Often an
underlying assumption in fake news discussion is that it is written to look
like real news, fooling the reader who does not check for reliability of the
sources or the arguments in its content. Through a unique study of three data
sets and features that capture the style and the language of articles, we show
that this assumption is not true. Fake news in most cases is more similar to
satire than to real news, leading us to conclude that persuasion in fake news
is achieved through heuristics rather than the strength of arguments. We show
overall title structure and the use of proper nouns in titles are very
significant in differentiating fake from real. This leads us to conclude that
fake news is targeted for audiences who are not likely to read beyond titles
and is aimed at creating mental associations between entities and claims.
Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation
Authors: Muneki Yasuda, Shun Kataoka
Date: 2017-03-28 00:00:00
Tasks: Form

In this paper, we address the inverse problem, or the statistical machine
learning problem, in Markov random fields with a non-parametric pair-wise
energy function with continuous variables. The inverse problem is formulated by
maximum likelihood estimation. The exact treatment of maximum likelihood
estimation is intractable because of two problems: (1) it includes the
evaluation of the partition function and (2) it is formulated in the form of
functional optimization. We avoid Problem (1) by using Bethe approximation.
Bethe approximation is an approximation technique equivalent to the loopy
belief propagation. Problem (2) can be solved by using orthonormal function
expansion. Orthonormal function expansion can reduce a functional optimization
problem to a function optimization problem. Our method can provide an analytic
form of the solution of the inverse problem within the framework of Bethe
approximation.
The Learning and Prediction of Application-level Traffic Data in Cellular Networks
Authors: Rongpeng Li, Zhifeng Zhao, Jianchao Zheng, Chengli Mei, Yueming Cai, Honggang Zhang
Date: 2016-06-15 00:00:00
Tasks: Dictionary Learning, Prediction, Traffic Prediction

Traffic learning and prediction is at the heart of the evaluation of the
performance of telecommunications networks and attracts a lot of attention in
wired broadband networks. Now, benefiting from the big data in cellular
networks, it becomes possible to make the analyses one step further into the
application level. In this paper, we firstly collect a significant amount of
application-level traffic data from cellular network operators. Afterwards,
with the aid of the traffic "big data", we make a comprehensive study over the
modeling and prediction framework of cellular network traffic. Our results
solidly demonstrate that there universally exist some traffic statistical
modeling characteristics, including ALPHA-stable modeled property in the
temporal domain and the sparsity in the spatial domain. Meanwhile, the results
also demonstrate the distinctions originated from the uniqueness of different
service types of applications. Furthermore, we propose a new traffic prediction
framework to encompass and explore these aforementioned characteristics and
then develop a dictionary learning-based alternating direction method to solve
it. Besides, we validate the prediction accuracy improvement and the robustness
of the proposed framework through extensive simulation results.
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
Authors: Shohei Kumagai, Kazuhiro Hotta, Takio Kurita
Date: 2017-03-28 00:00:00
Tasks: Crowd Counting

This paper proposes a crowd counting method. Crowd counting is difficult
because of large appearance changes of a target which caused by density and
scale changes. Conventional crowd counting methods generally utilize one
predictor (e,g., regression and multi-class classifier). However, such only one
predictor can not count targets with large appearance changes well. In this
paper, we propose to predict the number of targets using multiple CNNs
specialized to a specific appearance, and those CNNs are adaptively selected
according to the appearance of a test image. By integrating the selected CNNs,
the proposed method has the robustness to large appearance changes. In
experiments, we confirm that the proposed method can count crowd with lower
counting error than a CNN and integration of CNNs with fixed weights. Moreover,
we confirm that each predictor automatically specialized to a specific
appearance.
Fast Optimization of Wildfire Suppression Policies with SMAC
Authors: Sean McGregor, Rachel Houtman, Claire Montgomery, Ronald Metoyer, Thomas G. Dietterich
Date: 2017-03-28 00:00:00
Tasks: Management, SMAC, SMAC+

Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into multi-stakeholder negotiations is to provide a high-fidelity
simulation environment in which stakeholders can explore the space of
alternative policies and understand the tradeoffs therein. Such an environment
needs to support fast optimization of MDP policies so that users can adjust
reward functions and analyze the resulting optimal policies. This paper
assesses the suitability of SMAC---a black-box empirical function optimization
algorithm---for rapid optimization of MDP policies. The paper describes five
reward function components and four stakeholder constituencies. It then
introduces a parameterized class of policies that can be easily understood by
the stakeholders. SMAC is applied to find the optimal policy in this class for
the reward functions of each of the stakeholder constituencies. The results
confirm that SMAC is able to rapidly find good policies that make sense from
the domain perspective. Because the full-fidelity forest fire simulator is far
too expensive to support interactive optimization, SMAC is applied to a
surrogate model constructed from a modest number of runs of the full-fidelity
simulator. To check the quality of the SMAC-optimized policies, the policies
are evaluated on the full-fidelity simulator. The results confirm that the
surrogate values estimates are valid. This is the first successful optimization
of wildfire management policies using a full-fidelity simulation. The same
methodology should be applicable to other contentious natural resource
management problems where high-fidelity simulation is extremely expensive.
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
Authors: Shumeet Baluja, Ian Fischer
Date: 2017-03-28 00:00:00
Tasks: Diversity

Multiple different approaches of generating adversarial examples have been
proposed to attack deep neural networks. These approaches involve either
directly computing gradients with respect to the image pixels, or directly
solving an optimization on the image pixels. In this work, we present a
fundamentally new method for generating adversarial examples that is fast to
execute and provides exceptional diversity of output. We efficiently train
feed-forward neural networks in a self-supervised manner to generate
adversarial examples against a target network or set of networks. We call such
a network an Adversarial Transformation Network (ATN). ATNs are trained to
generate adversarial examples that minimally modify the classifier's outputs
given the original input, while constraining the new classification to match an
adversarial target class. We present methods to train ATNs and analyze their
effectiveness targeting a variety of MNIST classifiers as well as the latest
state-of-the-art ImageNet classifier Inception ResNet v2.
CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
Authors: Jiajun Lu, Aditya Deshpande, David Forsyth
Date: 2016-12-01 00:00:00
Tasks: Image Relighting

Problems such as predicting a new shading field (Y) for an image (X) are
ambiguous: many very distinct solutions are good. Representing this ambiguity
requires building a conditional model P(Y|X) of the prediction, conditioned on
the image. Such a model is difficult to train, because we do not usually have
training data containing many different shadings for the same image. As a
result, we need different training examples to share data to produce good
models. This presents a danger we call "code space collapse" - the training
procedure produces a model that has a very good loss score, but which
represents the conditional distribution poorly. We demonstrate an improved
method for building conditional models by exploiting a metric constraint on
training data that prevents code space collapse. We demonstrate our model on
two example tasks using real data: image saturation adjustment, image
relighting. We describe quantitative metrics to evaluate ambiguous generation
results. Our results quantitatively and qualitatively outperform different
strong baselines.
Deep attractor network for single-microphone speaker separation
Authors: Zhuo Chen, Yi Luo, Nima Mesgarani
Date: 2016-11-27 00:00:00
Tasks: Speaker Separation, Speech Separation

Despite the overwhelming success of deep learning in various speech
processing tasks, the problem of separating simultaneous speakers in a mixture
remains challenging. Two major difficulties in such systems are the arbitrary
source permutation and unknown number of sources in the mixture. We propose a
novel deep learning framework for single channel speech separation by creating
attractor points in high dimensional embedding space of the acoustic signals
which pull together the time-frequency bins corresponding to each source.
Attractor points in this study are created by finding the centroids of the
sources in the embedding space, which are subsequently used to determine the
similarity of each bin in the mixture to each source. The network is then
trained to minimize the reconstruction error of each source by optimizing the
embeddings. The proposed model is different from prior works in that it
implements an end-to-end training, and it does not depend on the number of
sources in the mixture. Two strategies are explored in the test time, K-means
and fixed attractor points, where the latter requires no post-processing and
can be implemented in real-time. We evaluated our system on Wall Street Journal
dataset and show 5.49\% improvement over the previous state-of-the-art methods.