pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
Authors: Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, Kostas Daniilidis
Date: 2018-12-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation

In this work, we propose a novel framework for unsupervised learning for
event cameras that learns motion information from only the event stream. In
particular, we propose an input representation of the events in the form of a
discretized volume that maintains the temporal distribution of the events,
which we pass through a neural network to predict the motion of the events.
This motion is used to attempt to remove any motion blur in the event image. We
then propose a loss function applied to the motion compensated event image that
measures the motion blur in this image. We train two networks with this
framework, one to predict optical flow, and one to predict egomotion and
depths, and evaluate these networks on the Multi Vehicle Stereo Event Camera
dataset, along with qualitative results from a variety of different scenes.
Magnetic Resonance Fingerprinting using Recurrent Neural Networks
Authors: Ilkay Oksuz, Gastao Cruz, James Clough, Aurelien Bustin, Nicolo Fuin, Rene M. Botnar, Claudia Prieto, Andrew P. King, Julia A. Schnabel
Date: 2018-12-19 00:00:00
Tasks: Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative
magnetic resonance imaging that allows simultaneous measurement of multiple
tissue properties in a single, time-efficient acquisition. Standard MRF
reconstructs parametric maps using dictionary matching and lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a recurrent neural network, which exploits the time-dependent information
of the MRF signal evolution. We evaluate our method on multiparametric
synthetic signals and compare it to existing MRF map reconstruction approaches,
including those based on neural networks. Our method achieves state-of-the-art
estimates of T1 and T2 values. In addition, the reconstruction time is
significantly reduced compared to dictionary-matching based approaches.
A Tour of Unsupervised Deep Learning for Medical Image Analysis
Authors: Khalid Raza, Nripendra Kumar Singh
Date: 2018-12-19 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Medical Image Analysis

Interpretation of medical images for diagnosis and treatment of complex
disease from high-dimensional and heterogeneous data remains a key challenge in
transforming healthcare. In the last few years, both supervised and
unsupervised deep learning achieved promising results in the area of medical
imaging and image analysis. Unlike supervised learning which is biased towards
how it is being supervised and manual efforts to create class label for the
algorithm, unsupervised learning derive insights directly from the data itself,
group the data and help to make data driven decisions without any external
bias. This review systematically presents various unsupervised models applied
to medical image analysis, including autoencoders and its several variants,
Restricted Boltzmann machines, Deep belief networks, Deep Boltzmann machine and
Generative adversarial network. Future research opportunities and challenges of
unsupervised techniques for medical image analysis have also been discussed.
Generating Diverse and Meaningful Captions
Authors: Annika Lindh, Robert J. Ross, Abhijit Mahalunkar, Giancarlo Salton, John D. Kelleher
Date: 2018-12-19 00:00:00
Tasks: Diversity, Image Captioning, Image Retrieval, Retrieval

Image Captioning is a task that requires models to acquire a multi-modal
understanding of the world and to express this understanding in natural
language text. While the state-of-the-art for this task has rapidly improved in
terms of n-gram metrics, these models tend to output the same generic captions
for similar images. In this work, we address this limitation and train a model
that generates more diverse and specific captions through an unsupervised
training approach that incorporates a learning signal from an Image Retrieval
model. We summarize previous results and improve the state-of-the-art on
caption diversity and novelty. We make our source code publicly available
online.
Very Power Efficient Neural Time-of-Flight
Authors: Yan Chen, Jimmy Ren, Xuanye Cheng, Keyuan Qian, Jinwei Gu
Date: 2018-12-19 00:00:00

Time-of-Flight (ToF) cameras require active illumination to obtain depth
information thus the power of illumination directly affects the performance of
ToF cameras. Traditional ToF imaging algorithms is very sensitive to
illumination and the depth accuracy degenerates rapidly with the power of it.
Therefore, the design of a power efficient ToF camera always creates a painful
dilemma for the illumination and the performance trade-off. In this paper, we
show that despite the weak signals in many areas under extreme short exposure
setting, these signals as a whole can be well utilized through a learning
process which directly translates the weak and noisy ToF camera raw to depth
map. This creates an opportunity to tackle the aforementioned dilemma and make
a very power efficient ToF camera possible. To enable the learning, we collect
a comprehensive dataset under a variety of scenes and photographic conditions
by a specialized ToF camera. Experiments show that our method is able to
robustly process ToF camera raw with the exposure time of one order of
magnitude shorter than that used in conventional ToF cameras. In addition to
evaluating our approach both quantitatively and qualitatively, we also discuss
its implication to designing the next generation power efficient ToF cameras.
We will make our dataset and code publicly available.
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks
Authors: Atsushi Yaguchi, Taiji Suzuki, Wataru Asano, Shuhei Nitta, Yukinobu Sakata, Akiyuki Tanizawa
Date: 2018-12-19 00:00:00
Tasks: Machine Translation, speech-recognition, Speech Recognition, Translation

In recent years, deep neural networks (DNNs) have been applied to various
machine leaning tasks, including image recognition, speech recognition, and
machine translation. However, large DNN models are needed to achieve
state-of-the-art performance, exceeding the capabilities of edge devices. Model
reduction is thus needed for practical use. In this paper, we point out that
deep learning automatically induces group sparsity of weights, in which all
weights connected to an output channel (node) are zero, when training DNNs
under the following three conditions: (1) rectified-linear-unit (ReLU)
activations, (2) an $L_2$-regularized objective function, and (3) the Adam
optimizer. Next, we analyze this behavior both theoretically and
experimentally, and propose a simple model reduction method: eliminate the zero
weights after training the DNN. In experiments on MNIST and CIFAR-10 datasets,
we demonstrate the sparsity with various training setups. Finally, we show that
our method can efficiently reduce the model size and performs well relative to
methods that use a sparsity-inducing regularizer.
MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
Authors: Hemant Kumar Aggarwal, Merry P. Mani, Mathews Jacob
Date: 2018-12-19 00:00:00
Tasks: Diffusion  MRI, Sensitivity

We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. The proposed algorithm is a generalization of existing MUSSELS algorithm with similar performance but with significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace thus exploiting the phase relations between shots. Due to the high computational complexity of self-learned filter bank, we propose to replace it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the phase relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state of the art methods while offering several orders of magnitude reduction in run-time.
On The Chain Rule Optimal Transport Distance
Authors: Frank Nielsen, Ke Sun
Date: 2018-12-19 00:00:00

We define a novel class of distances between statistical multivariate distributions by modeling an optimal transport problem on their marginals with respect to a ground distance defined on their conditionals. These new distances are metrics whenever the ground distance between the marginals is a metric, generalize both the Wasserstein distances between discrete measures and a recently introduced metric distance between statistical mixtures, and provide an upper bound for jointly convex distances between statistical mixtures. By entropic regularization of the optimal transport, we obtain a fast differentiable Sinkhorn-type distance. We experimentally evaluate our new family of distances by quantifying the upper bounds of several jointly convex distances between statistical mixtures, and by proposing a novel efficient method to learn Gaussian mixture models (GMMs) by simplifying kernel density estimators with respect to our distance. Our GMM learning technique experimentally improves significantly over the EM implementation of {\tt sklearn} on the {\tt MNIST} and {\tt Fashion MNIST} datasets.
The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data
Authors: Mario Amrehn, Firas Mualla, Elli Angelopoulou, Stefan Steidl, Andreas Maier
Date: 2018-12-19 00:00:00
Tasks: Prediction

Data analysis and machine learning have become an integrative part of the
modern scientific methodology, providing automated techniques to predict
further information based on observations. One of these classification and
regression techniques is the random forest approach. Those decision tree based
predictors are best known for their good computational performance and
scalability. However, in case of severely imbalanced training data, as often
seen in medical studies' data with large control groups, the training algorithm
or the sampling process has to be altered in order to improve the prediction
quality for minority classes. In this work, a balanced random forest approach
for WEKA is proposed. Furthermore, the prediction quality of the unmodified
random forest implementation and the new balanced random forest version for
WEKA are evaluated against reference implementations in R. Two-class problems
on balanced data sets and imbalanced medical studies' data are investigated. A
superior prediction quality using the proposed method for imbalanced data is
shown compared to the other three techniques.
Window detection in aerial texture images of the Berlin 3D CityGML Model
Authors: Franziska Lippoldt, Marius Erdt
Date: 2018-12-19 00:00:00
Tasks: Region Proposal

This article explores the usage of the state-of-art neural network Mask R-CNN
to be used for window detection of texture files from the CityGML model of
Berlin. As texture files are very irregular in terms of size, exposure settings
and orientation, we use several parameter optimisation methods to improve the
precision. Those textures are cropped from aerial photos, which implies that
the angle of the facade, the exposure as well as contrast are calibrated
towards the mean and not towards the single facade. The analysis of a single
texture image with the human eye itself is challenging: A combination of window
and facade estimation and perspective analysis is necessary in order to
determine the facades and windows. We train and detect bounding boxes and masks
from two data sets with image size 128 and 256. We explore various
configuration optimisation methods and the relation of the Region Proposal
Network, detected ROIs and the mask output. Our final results shows that the we
can improve the average precision scores for both data set sizes, yet the
initial AP score varies and leads to different resulting scores.
Shallow Cue Guided Deep Visual Tracking via Mixed Models
Authors: Fangwen Tu, Shuzhi Sam Ge, Chang Chieh Hang
Date: 2018-12-19 00:00:00
Tasks: Visual Tracking

In this paper, a robust visual tracking approach via mixed model based
convolutional neural networks (SDT) is developed. In order to handle abrupt or
fast motion, a prior map is generated to facilitate the localization of region
of interest (ROI) before the deep tracker is performed. A top-down saliency
model with nineteen shallow cues are employed to construct the prior map with
online learnt combination weights. Moreover, apart from a holistic deep
learner, four local networks are also trained to learn different components of
the target. The generated four local heat maps will facilitate to rectify the
holistic map by eliminating the distracters to avoid drifting. Furthermore, to
guarantee the instance for online update of high quality, a prioritised update
strategy is implemented by casting the problem into a label noise problem. The
selection probability is designed by considering both confidence values and
bio-inspired memory for temporal information integration. Experiments are
conducted qualitatively and quantitatively on a set of challenging image
sequences. Comparative study demonstrates that the proposed algorithm
outperforms other state-of-the-art methods.
A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
Authors: Martin Gerlach, Francesc Font-Clos
Date: 2018-12-19 00:00:00
Tasks: Information Retrieval, Retrieval

The use of Project Gutenberg (PG) as a text corpus has been extremely popular
in statistical analysis of language for more than 25 years. However, in
contrast to other major linguistic datasets of similar importance, no
consensual full version of PG exists to date. In fact, most PG studies so far
either consider only a small number of manually selected books, leading to
potential biased subsets, or employ vastly different pre-processing strategies
(often specified in insufficient details), raising concerns regarding the
reproducibility of published results. In order to address these shortcomings,
here we present the Standardized Project Gutenberg Corpus (SPGC), an open
science approach to a curated version of the complete PG data containing more
than 50,000 books and more than $3 \times 10^9$ word-tokens. Using different
sources of annotated metadata, we not only provide a broad characterization of
the content of PG, but also show different examples highlighting the potential
of SPGC for investigating language variability across time, subjects, and
authors. We publish our methodology in detail, the code to download and process
the data, as well as the obtained corpus itself on 3 different levels of
granularity (raw text, timeseries of word tokens, and counts of words). In this
way, we provide a reproducible, pre-processed, full-size version of Project
Gutenberg as a new scientific resource for corpus linguistics, natural language
processing, and information retrieval.
Multitask Painting Categorization by Deep Multibranch Neural Network
Authors: Simone Bianco, Davide Mazzini, Paolo Napoletano, Raimondo Schettini
Date: 2018-12-19 00:00:00

In this work we propose a new deep multibranch neural network to solve the
tasks of artist, style, and genre categorization in a multitask formulation. In
order to gather clues from low-level texture details and, at the same time,
exploit the coarse layout of the painting, the branches of the proposed
networks are fed with crops at different resolutions. We propose and compare
two different crop strategies: the first one is a random-crop strategy that
permits to manage the tradeoff between accuracy and speed; the second one is a
smart extractor based on Spatial Transformer Networks trained to extract the
most representative subregions. Furthermore, inspired by the results obtained
in other domains, we experiment the joint use of hand-crafted features directly
computed on the input images along with neural ones. Experiments are performed
on a new dataset originally sourced from wikiart.org and hosted by Kaggle, and
made suitable for artist, style and genre multitask learning. The dataset here
proposed, named MultitaskPainting100k, is composed by 100K paintings, 1508
artists, 125 styles and 41 genres. Our best method, tested on the
MultitaskPainting100k dataset, achieves accuracy levels of 56.5%, 57.2%, and
63.6% on the tasks of artist, style and genre prediction respectively.
Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study
Authors: Maral Dadvar, Kai Eckert
Date: 2018-12-19 00:00:00
Tasks: 4k, BIG-bench Machine Learning, Deep Learning

Cyberbullying is a disturbing online misbehaviour with troubling
consequences. It appears in different forms, and in most of the social
networks, it is in textual format. Automatic detection of such incidents
requires intelligent systems. Most of the existing studies have approached this
problem with conventional machine learning models and the majority of the
developed models in these studies are adaptable to a single social network at a
time. In recent studies, deep learning based models have found their way in the
detection of cyberbullying incidents, claiming that they can overcome the
limitations of the conventional models, and improve the detection performance.
In this paper, we investigate the findings of a recent literature in this
regard. We successfully reproduced the findings of this literature and
validated their findings using the same datasets, namely Wikipedia, Twitter,
and Formspring, used by the authors. Then we expanded our work by applying the
developed methods on a new YouTube dataset (~54k posts by ~4k users) and
investigated the performance of the models in new social media platforms. We
also transferred and evaluated the performance of the models trained on one
platform to another platform. Our findings show that the deep learning based
models outperform the machine learning models previously applied to the same
YouTube dataset. We believe that the deep learning based models can also
benefit from integrating other sources of information and looking into the
impact of profile information of the users in social networks.
FrameNet automatic analysis : a study on a French corpus of encyclopedic texts
Authors: Gabriel Marzinotto, Géraldine Damnati, Frederic Bechet
Date: 2018-12-19 00:00:00
Tasks: feature selection

This article presents an automatic frame analysis system evaluated on a
corpus of French encyclopedic history texts annotated according to the FrameNet
formalism. The chosen approach relies on an integrated sequence labeling model
which jointly optimizes frame identification and semantic role segmentation and
identification. The purpose of this study is to analyze the task complexity
from several dimensions. Hence we provide detailed evaluations from a feature
selection point of view and from the data point of view.
Learning beamforming in ultrasound imaging
Authors: Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky
Date: 2018-12-19 00:00:00
Tasks: Image Reconstruction

Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demonstrate that replacing the traditional ultrasound processing pipeline with a data-driven, learnable counterpart leads to significant improvement in image quality. Moreover, we demonstrate that greater improvement can be achieved through a learning-based design of the transmitted beam patterns simultaneously with learning an image reconstruction pipeline. We evaluate our method on an in-vivo first-harmonic cardiac ultrasound dataset acquired from volunteers and demonstrate the significance of the learned pipeline and transmit beam patterns on the image quality when compared to standard transmit and receive beamformers used in high frame-rate US imaging. We believe that the presented methodology provides a fundamentally different perspective on the classical problem of ultrasound beam pattern design.
Credibility evaluation of income data with hierarchical correlation reconstruction
Authors: Jarek Duda, Adam Szulc
Date: 2018-12-19 00:00:00

In situations like tax declarations or analyzes of household budgets we would
like to automatically evaluate credibility of exogenous variable (declared
income) based on some available (endogenous) variables - we want to build a
model and train it on provided data sample to predict (conditional) probability
distribution of exogenous variable based on values of endogenous variables.
Using Polish household budget survey data there will be discussed simple and
systematic adaptation of hierarchical correlation reconstruction (HCR)
technique for this purpose, which allows to combine interpretability of
statistics with modelling of complex densities like in machine learning. For
credibility evaluation we normalize marginal distribution of predicted variable
to $\rho\approx 1$ uniform distribution on $[0,1]$ using empirical distribution
function $(x=EDF(y)\in[0,1])$, then model density of its conditional
distribution $(\textrm{Pr}(x_0|x_1 x_2\ldots))$ as a linear combination of
orthonormal polynomials using coefficients modelled as linear combinations of
features of the remaining variables. These coefficients can be calculated
independently, have similar interpretation as cumulants, additionally allowing
to directly reconstruct probability distribution. Values corresponding to high
predicted density can be considered as credible, while low density suggests
disagreement with statistics of data sample, for example to mark for manual
verification a chosen percentage of data points evaluated as the least
credible.
Semantic Frame Parsing for Information Extraction : the CALOR corpus
Authors: Gabriel Marzinotto, Jeremy Auguste, Frederic Bechet, Géraldine Damnati, Alexis Nasr
Date: 2018-12-19 00:00:00
Proceeding: LREC 2018 5
Tasks: Semantic Frame Parsing, Semantic Parsing

This paper presents a publicly available corpus of French encyclopedic
history texts annotated according to the Berkeley FrameNet formalism. The main
difference in our approach compared to previous works on semantic parsing with
FrameNet is that we are not interested here in full text parsing but rather on
partial parsing. The goal is to select from the FrameNet resources the minimal
set of frames that are going to be useful for the applicative framework
targeted, in our case Information Extraction from encyclopedic documents. Such
an approach leverages the manual annotation of larger corpora than those
obtained through full text parsing and therefore opens the door to alternative
methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark
corpus. The approaches compared in this study rely on an integrated sequence
labeling model which jointly optimizes frame identification and semantic role
segmentation and identification. The models compared are CRFs and multitasks
bi-LSTMs.