pwc-archive/papers-with-abstracts huggingface dataset:
Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks
Authors: Davis M. Vigneault, Weidi Xie, Carolyn Y. Ho, David A. Bluemke, J. Alison Noble
Date: 2017-11-03 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Pixelwise segmentation of the left ventricular (LV) myocardium and the four
cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is
an essential preprocessing step for a wide range of analyses. Variability in
contrast, appearance, orientation, and placement of the heart between patients,
clinical views, scanners, and protocols makes fully automatic semantic
segmentation a notoriously difficult problem. Here, we present ${\Omega}$-Net
(Omega-Net): a novel convolutional neural network (CNN) architecture for
simultaneous localization, transformation into a canonical orientation, and
semantic segmentation. First, an initial segmentation is performed on the input
image, second, the features learned during this initial segmentation are used
to predict the parameters needed to transform the input image into a canonical
orientation, and third, a final segmentation is performed on the transformed
image. In this work, ${\Omega}$-Nets of varying depths were trained to detect
five foreground classes in any of three clinical views (short axis, SA,
four-chamber, 4C, two-chamber, 2C), without prior knowledge of the view being
segmented. The architecture was trained on a cohort of patients with
hypertrophic cardiomyopathy and healthy control subjects. Network performance
as measured by weighted foreground intersection-over-union (IoU) was
substantially improved in the best-performing ${\Omega}$- Net compared with
U-Net segmentation without localization or orientation. In addition,
{\Omega}-Net was retrained from scratch on the 2017 MICCAI ACDC dataset, and
achieves state-of-the-art results on the LV and RV bloodpools, and performed
slightly worse in segmentation of the LV myocardium. We conclude this
architecture represents a substantive advancement over prior approaches, with
implications for biomedical image segmentation more generally.
Gaussian Processes Over Graphs
Authors: Arun Venkitaraman, Saikat Chatterjee, Peter Händel
Date: 2018-03-15 00:00:00
Tasks: Gaussian Processes

We propose Gaussian processes for signals over graphs (GPG) using the apriori
knowledge that the target vectors lie over a graph. We incorporate this
information using a graph- Laplacian based regularization which enforces the
target vectors to have a specific profile in terms of graph Fourier transform
coeffcients, for example lowpass or bandpass graph signals. We discuss how the
regularization affects the mean and the variance in the prediction output. In
particular, we prove that the predictive variance of the GPG is strictly
smaller than the conventional Gaussian process (GP) for any non-trivial graph.
We validate our concepts by application to various real-world graph signals.
Our experiments show that the performance of the GPG is superior to GP for
small training data sizes and under noisy training.
Progressive Structure from Motion
Authors: Alex Locher, Michal Havlena, Luc van Gool
Date: 2018-03-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Reconstruction

Structure from Motion or the sparse 3D reconstruction out of individual
photos is a long studied topic in computer vision. Yet none of the existing
reconstruction pipelines fully addresses a progressive scenario where images
are only getting available during the reconstruction process and intermediate
results are delivered to the user. Incremental pipelines are capable of growing
a 3D model but often get stuck in local minima due to wrong (binding) decisions
taken based on incomplete information. Global pipelines on the other hand need
the access to the complete viewgraph and are not capable of delivering
intermediate results. In this paper we propose a new reconstruction pipeline
working in a progressive manner rather than in a batch processing scheme. The
pipeline is able to recover from failed reconstructions in early stages, avoids
to take binding decisions, delivers a progressive output and yet maintains the
capabilities of existing pipelines. We demonstrate and evaluate our method on
diverse challenging public and dedicated datasets including those with highly
symmetric structures and compare to the state of the art.
Frank-Wolfe with Subsampling Oracle
Authors: Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont
Date: 2018-03-20 00:00:00
Proceeding: ICML 2018 7

We analyze two novel randomized variants of the Frank-Wolfe (FW) or
conditional gradient algorithm. While classical FW algorithms require solving a
linear minimization problem over the domain at each iteration, the proposed
method only requires to solve a linear minimization problem over a small
\emph{subset} of the original domain. The first algorithm that we propose is a
randomized variant of the original FW algorithm and achieves a
$\mathcal{O}(1/t)$ sublinear convergence rate as in the deterministic
counterpart. The second algorithm is a randomized variant of the Away-step FW
algorithm, and again as its deterministic counterpart, reaches linear (i.e.,
exponential) convergence rate making it the first provably convergent
randomized variant of Away-step FW. In both cases, while subsampling reduces
the convergence rate by a constant factor, the linear minimization step can be
a fraction of the cost of that of the deterministic versions, especially when
the data is streamed. We illustrate computational gains of the algorithms on
regression problems, involving both $\ell_1$ and latent group lasso penalties.
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Authors: Jonas Rauber, Wieland Brendel, Matthias Bethge
Date: 2017-07-13 00:00:00
Tasks: Adversarial Attack, BIG-bench Machine Learning

Even todays most advanced machine learning models are easily fooled by almost
imperceptible perturbations of their inputs. Foolbox is a new Python package to
generate such adversarial perturbations and to quantify and compare the
robustness of machine learning models. It is build around the idea that the
most comparable robustness measure is the minimum perturbation needed to craft
an adversarial example. To this end, Foolbox provides reference implementations
of most published adversarial attack methods alongside some new ones, all of
which perform internal hyperparameter tuning to find the minimum adversarial
perturbation. Additionally, Foolbox interfaces with most popular deep learning
frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows
different adversarial criteria such as targeted misclassification and top-k
misclassification as well as different distance measures. The code is licensed
under the MIT license and is openly available at
https://github.com/bethgelab/foolbox . The most up-to-date documentation can be
found at http://foolbox.readthedocs.io .
Variance Networks: When Expectation Does Not Meet Your Expectations
Authors: Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
Date: 2018-03-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: Efficient Exploration, Reinforcement Learning, Reinforcement Learning (RL)

Ordinary stochastic neural networks mostly rely on the expected values of
their weights to make predictions, whereas the induced noise is mostly used to
capture the uncertainty, prevent overfitting and slightly boost the performance
through test-time averaging. In this paper, we introduce variance layers, a
different kind of stochastic layers. Each weight of a variance layer follows a
zero-mean distribution and is only parameterized by its variance. We show that
such layers can learn surprisingly well, can serve as an efficient exploration
tool in reinforcement learning tasks and provide a decent defense against
adversarial attacks. We also show that a number of conventional Bayesian neural
networks naturally converge to such zero-mean posteriors. We observe that in
these cases such zero-mean parameterization leads to a much better training
objective than conventional parameterizations where the mean is being learned.
An ILP Solver for Multi-label MRFs with Connectivity Constraints
Authors: Ruobing Shen, Eric Kendinibilir, Ismail Ben Ayed, Andrea Lodi, Andrea Tramontani, Gerhard Reinelt
Date: 2017-12-16 00:00:00
Tasks: Weakly supervised segmentation

Integer Linear Programming (ILP) formulations of Markov random fields (MRFs)
models with global connectivity priors were investigated previously in computer
vision, e.g., \cite{globalinter,globalconn}. In these works, only Linear
Programing (LP) relaxations \cite{globalinter,globalconn} or simplified
versions \cite{graphcutbase} of the problem were solved. This paper
investigates the ILP of multi-label MRF with exact connectivity priors via a
branch-and-cut method, which provably finds globally optimal solutions. The
method enforces connectivity priors iteratively by a cutting plane method, and
provides feasible solutions with a guarantee on sub-optimality even if we
terminate it earlier. The proposed ILP can be applied as a post-processing
method on top of any existing multi-label segmentation approach. As it provides
globally optimal solution, it can be used off-line to generate ground-truth
labeling, which serves as quality check for any fast on-line algorithm.
Furthermore, it can be used to generate ground-truth proposals for weakly
supervised segmentation. We demonstrate the power and usefulness of our model
by several experiments on the BSDS500 and PASCAL image dataset, as well as on
medical images with trained probability maps.
Are GANs Created Equal? A Large-Scale Study
Authors: Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Date: 2017-11-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Hyperparameter Optimization

Generative adversarial networks (GAN) are a powerful subclass of generative
models. Despite a very rich research activity leading to numerous interesting
GAN algorithms, it is still very hard to assess which algorithm(s) perform
better than others. We conduct a neutral, multi-faceted large-scale empirical
study on state-of-the art models and evaluation measures. We find that most
models can reach similar scores with enough hyperparameter optimization and
random restarts. This suggests that improvements can arise from a higher
computational budget and tuning more than fundamental algorithmic changes. To
overcome some limitations of the current metrics, we also propose several data
sets on which precision and recall can be computed. Our experimental results
suggest that future GAN research should be based on more systematic and
objective evaluation procedures. Finally, we did not find evidence that any of
the tested algorithms consistently outperforms the non-saturating GAN
introduced in \cite{goodfellow2014generative}.
Information content of coevolutionary game landscapes
Authors: Hendrik Richter
Date: 2018-03-20 00:00:00

Coevolutionary game dynamics is the result of players that may change their
strategies and their network of interaction. For such games, and based on
interpreting strategies as configurations, strategy-to-payoff maps can be
defined for every interaction network, which opens up to derive game
landscapes. This paper presents an analysis of these game landscapes by their
information content. By this analysis, we particularly study the effect of a
rescaled payoff matrix generalizing social dilemmas and differences between
well-mixed and structured populations.
Fix your classifier: the marginal value of training the last weight layer
Authors: Elad Hoffer, Itay Hubara, Daniel Soudry
Date: 2018-01-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

Neural networks are commonly used as models for classification for a wide
variety of tasks. Typically, a learned affine transformation is placed at the
end of such models, yielding a per-class value used for classification. This
classifier can have a vast number of parameters, which grows linearly with the
number of possible classes, thus requiring increasingly more resources. In this
work we argue that this classifier can be fixed, up to a global scale constant,
with little or no loss of accuracy for most tasks, allowing memory and
computational benefits. Moreover, we show that by initializing the classifier
with a Hadamard matrix we can speed up inference as well. We discuss the
implications for current understanding of neural network models.
Segmentation of histological images and fibrosis identification with a convolutional neural network
Authors: Xiaohang Fu, Tong Liu, Zhaohan Xiong, Bruce H. Smaill, Martin K. Stiles, Jichao Zhao
Date: 2018-03-20 00:00:00
Tasks: Segmentation

Segmentation of histological images is one of the most crucial tasks for many
biomedical analyses including quantification of certain tissue type. However,
challenges are posed by high variability and complexity of structural features
in such images, in addition to imaging artifacts. Further, the conventional
approach of manual thresholding is labor-intensive, and highly sensitive to
inter- and intra-image intensity variations. An accurate and robust automated
segmentation method is of high interest. We propose and evaluate an elegant
convolutional neural network (CNN) designed for segmentation of histological
images, particularly those with Masson's trichrome stain. The network comprises
of 11 successive convolutional - rectified linear unit - batch normalization
layers, and outperformed state-of-the-art CNNs on a dataset of cardiac
histological images (labeling fibrosis, myocytes, and background) with a Dice
similarity coefficient of 0.947. With 100 times fewer (only 300 thousand)
trainable parameters, our CNN is less susceptible to overfitting, and is
efficient. Additionally, it retains image resolution from input to output,
captures fine-grained details, and can be trained end-to-end smoothly. To the
best of our knowledge, this is the first deep CNN tailored for the problem of
concern, and may be extended to solve similar segmentation tasks to facilitate
investigations into pathology and clinical treatment.
Expressivity in TTS from Semantics and Pragmatics
Authors: Rodolfo Delmonte
Date: 2018-03-20 00:00:00

In this paper we present ongoing work to produce an expressive TTS reader
that can be used both in text and dialogue applications. The system called
SPARSAR has been used to read (English) poetry so far but it can now be applied
to any text. The text is fully analyzed both at phonetic and phonological
level, and at syntactic and semantic level. In addition, the system has access
to a restricted list of typical pragmatically marked phrases and expressions
that are used to convey specific discourse function and speech acts and need
specialized intonational contours. The text is transformed into a poem-like
structures, where each line corresponds to a Breath Group, semantically and
syntactically consistent. Stanzas correspond to paragraph boundaries.
Analogical parameters are related to ToBI theoretical indices but their number
is doubled. In this paper, we concentrate on short stories and fables.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Authors: Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-yan Yeung
Date: 2018-03-20 00:00:00
Tasks: General Classification, Node Classification

We propose a new network architecture, Gated Attention Networks (GaAN), for
learning on graphs. Unlike the traditional multi-head attention mechanism,
which equally consumes all attention heads, GaAN uses a convolutional
sub-network to control each attention head's importance. We demonstrate the
effectiveness of GaAN on the inductive node classification problem. Moreover,
with GaAN as a building block, we construct the Graph Gated Recurrent Unit
(GGRU) to address the traffic speed forecasting problem. Extensive experiments
on three real-world datasets show that our GaAN framework achieves
state-of-the-art results on both tasks.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Authors: Jianming Lv, Weihang Chen, Qing Li, Can Yang
Date: 2018-03-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Incremental Learning, Learning-To-Rank, Person Re-Identification, Transfer Learning, Unsupervised Person Re-Identification

Most of the proposed person re-identification algorithms conduct supervised
training and testing on single labeled datasets with small size, so directly
deploying these trained models to a large-scale real-world camera network may
lead to poor performance due to underfitting. It is challenging to
incrementally optimize the models by using the abundant unlabeled data
collected from the target domain. To address this challenge, we propose an
unsupervised incremental learning algorithm, TFusion, which is aided by the
transfer learning of the pedestrians' spatio-temporal patterns in the target
domain. Specifically, the algorithm firstly transfers the visual classifier
trained from small labeled source dataset to the unlabeled target dataset so as
to learn the pedestrians' spatial-temporal patterns. Secondly, a Bayesian
fusion model is proposed to combine the learned spatio-temporal patterns with
visual features to achieve a significantly improved classifier. Finally, we
propose a learning-to-rank based mutual promotion procedure to incrementally
optimize the classifiers based on the unlabeled data in the target domain.
Comprehensive experiments based on multiple real surveillance datasets are
conducted, and the results show that our algorithm gains significant
improvement compared with the state-of-art cross-dataset unsupervised person
re-identification algorithms.
Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
Authors: Fabian Groh, Patrick Wieschollek, Hendrik P. A. Lensch
Date: 2018-03-20 00:00:00
Tasks: Classify 3D Point Clouds, GPU, Semantic Segmentation

Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design choices from 2D-image learning methods towards processing point clouds are not readily possible. In this work, we introduce a natural generalization flex-convolution of the conventional convolution layer along with an efficient GPU implementation. We demonstrate competitive performance on rather small benchmark sets using fewer parameters and lower memory consumption and obtain significant improvements on a million-scale real-world dataset. Ours is the first which allows to efficiently process 7 million points concurrently.
Integral Human Pose Regression
Authors: Xiao Sun, Bin Xiao, Fangyin Wei, Shuang Liang, Yichen Wei
Date: 2017-11-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation, Quantization, regression

State-of-the-art human pose estimation methods are based on heat map
representation. In spite of the good performance, the representation has a few
issues in nature, such as not differentiable and quantization error. This work
shows that a simple integral operation relates and unifies the heat map
representation and joint regression, thus avoiding the above issues. It is
differentiable, efficient, and compatible with any heat map based methods. Its
effectiveness is convincingly validated via comprehensive ablation experiments
under various settings, specifically on 3D pose estimation, for the first time.
eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing
Authors: Matteo Negri, Marco Turchi, Rajen Chatterjee, Nicola Bertoldi
Date: 2018-03-20 00:00:00
Proceeding: LREC 2018 5
Tasks: Automatic Post-Editing, Sentence

Training models for the automatic correction of machine-translated text
usually relies on data consisting of (source, MT, human post- edit) triplets
providing, for each source sentence, examples of translation errors with the
corresponding corrections made by a human post-editor. Ideally, a large amount
of data of this kind should allow the model to learn reliable correction
patterns and effectively apply them at test stage on unseen (source, MT) pairs.
In practice, however, their limited availability calls for solutions that also
integrate in the training process other sources of knowledge. Along this
direction, state-of-the-art results have been recently achieved by systems
that, in addition to a limited amount of available training data, exploit
artificial corpora that approximate elements of the "gold" training instances
with automatic translations. Following this idea, we present eSCAPE, the
largest freely-available Synthetic Corpus for Automatic Post-Editing released
so far. eSCAPE consists of millions of entries in which the MT element of the
training triplets has been obtained by translating the source side of
publicly-available parallel corpora, and using the target side as an artificial
human post-edit. Translations are obtained both with phrase-based and neural
models. For each MT paradigm, eSCAPE contains 7.2 million triplets for
English-German and 3.3 millions for English-Italian, resulting in a total of
14,4 and 6,6 million instances respectively. The usefulness of eSCAPE is proved
through experiments in a general-domain scenario, the most challenging one for
automatic post-editing. For both language directions, the models trained on our
artificial data always improve MT quality with statistically significant gains.
The current version of eSCAPE can be freely downloaded from:
http://hltshare.fbk.eu/QT21/eSCAPE.html.
Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
Authors: Andrew Lensen, Bing Xue, Mengjie Zhang
Date: 2018-02-02 00:00:00
Tasks: feature selection

Recently, feature selection has become an increasingly important area of
research due to the surge in high-dimensional datasets in all areas of modern
life. A plethora of feature selection algorithms have been proposed, but it is
difficult to truly analyse the quality of a given algorithm. Ideally, an
algorithm would be evaluated by measuring how well it removes known bad
features. Acquiring datasets with such features is inherently difficult, and so
a common technique is to add synthetic bad features to an existing dataset.
While adding noisy features is an easy task, it is very difficult to
automatically add complex, redundant features. This work proposes one of the
first approaches to generating redundant features, using a novel genetic
programming approach. Initial experiments show that our proposed method can
automatically create difficult, redundant features which have the potential to
be used for creating high-quality feature selection benchmark datasets.