pwc-archive/papers-with-abstracts huggingface dataset:
RNN-based Generative Model for Fine-Grained Sketching
Authors: Andrin Jenal, Nikolay Savinov, Torsten Sattler, Gaurav Chaurasia
Date: 2019-01-13 00:00:00

Deep generative models have shown great promise when it comes to synthesising
novel images. While they can generate images that look convincing on a
higher-level, generating fine-grained details is still a challenge. In order to
foster research on more powerful generative approaches, this paper proposes a
novel task: generative modelling of 2D tree skeletons. Trees are an interesting
shape class because they exhibit complexity and variations that are well-suited
to measure the ability of a generative model to generated detailed structures.
We propose a new dataset for this task and demonstrate that state-of-the-art
generative models fail to synthesise realistic images on our benchmark, even
though they perform well on current datasets like MNIST digits. Motivated by
these results, we propose a novel network architecture based on combining a
variational autoencoder using Recurrent Neural Networks and a convolutional
discriminator. The network, error metrics and training procedure are adapted to
the task of fine-grained sketching. Through quantitative and perceptual
experiments, we show that our model outperforms previous work and that our
dataset is a valuable benchmark for generative models. We will make our dataset
publicly available.
Generating Adversarial Perturbation with Root Mean Square Gradient
Authors: Yatie Xiao, Chi-Man Pun, Jizhe Zhou
Date: 2019-01-13 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

We focus our attention on the problem of generating adversarial perturbations based on the gradient in image classification domain
A Fully Bayesian Infinite Generative Model for Dynamic Texture Segmentation
Authors: Sahar Yousefi, M. T. Manzuri Shalmani, Antoni B. Chan
Date: 2019-01-13 00:00:00
Tasks: Segmentation

Generative dynamic texture models (GDTMs) are widely used for dynamic texture
(DT) segmentation in the video sequences. GDTMs represent DTs as a set of
linear dynamical systems (LDSs). A major limitation of these models concerns
the automatic selection of a proper number of DTs. Dirichlet process mixture
(DPM) models which have appeared recently as the cornerstone of the
non-parametric Bayesian statistics, is an optimistic candidate toward resolving
this issue. Under this motivation to resolve the aforementioned drawback, we
propose a novel non-parametric fully Bayesian approach for DT segmentation,
formulated on the basis of a joint DPM and GDTM construction. This interaction
causes the algorithm to overcome the problem of automatic segmentation
properly. We derive the Variational Bayesian Expectation-Maximization (VBEM)
inference for the proposed model. Moreover, in the E-step of inference, we
apply Rauch-Tung-Striebel smoother (RTSS) algorithm on Variational Bayesian
LDSs. Ultimately, experiments on different video sequences are performed.
Experiment results indicate that the proposed algorithm outperforms the
previous methods in efficiency and accuracy noticeably.
Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation
Authors: Jingwei Gan, Pai Liu, Rajan K. Chakrabarty
Date: 2019-01-13 00:00:00
Tasks: Generative Adversarial Network

We introduce a generative adversarial network (GAN) model to simulate the
3-dimensional Lagrangian motion of particles trapped in the recirculation zone
of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent
neural network, serving as a generator, and a convoluted neural network,
serving as a discriminator. Adversarial training was performed to the point
where the best-trained discriminator failed to distinguish the ground truth
from the trajectory produced by the best-trained generator. The model
performance was then benchmarked against a statistical analysis performed on
both the simulated trajectories and the ground truth, with regard to the
accuracy and generalization criteria.
Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing
Authors: Yunxuan Xiao, Yikai Li, Yuwei Wu, LiZhen Zhu
Date: 2019-01-13 00:00:00
Tasks: Image Matting, Position, Scene Parsing

Replacing the background and simultaneously adjusting foreground objects is a
challenging task in image editing. Current techniques for generating such
images relies heavily on user interactions with image editing softwares, which
is a tedious job for professional retouchers. To reduce their workload, some
exciting progress has been made on generating images with a given background.
However, these models can neither adjust the position and scale of the
foreground objects, nor guarantee the semantic consistency between foreground
and background. To overcome these limitations, we propose a framework --
ART(Auto-Retoucher), to generate images with sufficient semantic and spatial
consistency. Images are first processed by semantic matting and scene parsing
modules, then a multi-task verifier model will give two confidence scores for
the current background and position setting. We demonstrate that our jointly
optimized verifier model successfully improves the visual consistency, and our
ART framework performs well on images with the human body as foregrounds.
Light-Field for RF
Authors: Manikanta Kotaru, Guy Satat, Ramesh Raskar, Sachin Katti
Date: 2019-01-13 00:00:00
Tasks: Privacy Preserving, RF-based Pose Estimation

Most computer vision systems and computational photography systems are
visible light based which is a small fraction of the electromagnetic (EM)
spectrum. In recent years radio frequency (RF) hardware has become more widely
available, for example, many cars are equipped with a RADAR, and almost every
home has a WiFi device. In the context of imaging, RF spectrum holds many
advantages compared to visible light systems. In particular, in this regime, EM
energy effectively interacts in different ways with matter. This property
allows for many novel applications such as privacy preserving computer vision
and imaging through absorbing and scattering materials in visible light such as
walls. Here, we expand many of the concepts in computational photography in
visible light to RF cameras. The main limitation of imaging with RF is the
large wavelength that limits the imaging resolution when compared to visible
light. However, the output of RF cameras is usually processed by computer
vision and perception algorithms which would benefit from multi-modal sensing
of the environment, and from sensing in situations in which visible light
systems fail. To bridge the gap between computational photography and RF
imaging, we expand the concept of light-field to RF. This work paves the way to
novel computational sensing systems with RF.
Image retrieval method based on CNN and dimension reduction
Authors: Zhihao Cao, Shaomin Mu, Yongyu Xu, Mengping Dong
Date: 2019-01-13 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval

An image retrieval method based on convolution neural network and dimension
reduction is proposed in this paper. Convolution neural network is used to
extract high-level features of images, and to solve the problem that the
extracted feature dimensions are too high and have strong correlation,
multilinear principal component analysis is used to reduce the dimension of
features. The features after dimension reduction are binary hash coded for fast
image retrieval. Experiments show that the method proposed in this paper has
better retrieval effect than the retrieval method based on principal component
analysis on the e-commerce image datasets.
Semi-Supervised Regression using Cluster Ensemble and Low-Rank Co-Association Matrix Decomposition under Uncertainties
Authors: Vladimir Berikov, Alexander Litvinenko
Date: 2019-01-13 00:00:00
Tasks: regression

In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of the ensemble is calculated on both labeled and unlabeled data; this matrix is used as a similarity matrix in the regularization framework to derive the predicted outputs. We use the low-rank decomposition of the co-association matrix to significantly speedup calculations and reduce memory. Numerical experiments using the Monte Carlo approach demonstrate robustness, efficiency, and scalability of the proposed method.
Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
Authors: Joshua J. Engelsma, Anil K. Jain
Date: 2019-01-13 00:00:00
Tasks: One-Class Classification, One-class classifier

Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
To mitigate these attacks, automated spoof detectors are trained to distinguish
a set of live or bona fide fingerprints from a set of known spoof fingerprints.
Despite their success, spoof detectors remain vulnerable when exposed to
attacks from spoofs made with materials not seen during training of the
detector. To alleviate this shortcoming, we approach spoof detection as a
one-class classification problem. The goal is to train a spoof detector on only
the live fingerprints such that once the concept of "live" has been learned,
spoofs of any material can be rejected. We accomplish this through training
multiple generative adversarial networks (GANS) on live fingerprint images
acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint
reader. Our experimental results, conducted on 5.5K spoof images (from 12
materials) and 11.8K live images show that the proposed approach improves the
cross-material spoof detection performance over state-of-the-art one-class and
binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing
materials, respectively.
Automated Deep Photo Style Transfer
Authors: Sebastian Penhouët, Paul Sanzenbacher
Date: 2019-01-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Style Transfer

Photorealism is a complex concept that cannot easily be formulated
mathematically. Deep Photo Style Transfer is an attempt to transfer the style
of a reference image to a content image while preserving its photorealism. This
is achieved by introducing a constraint that prevents distortions in the
content image and by applying the style transfer independently for semantically
different parts of the images. In addition, an automated segmentation process
is presented that consists of a neural network based segmentation method
followed by a semantic grouping step. To further improve the results a measure
for image aesthetics is used and elaborated. If the content and the style image
are sufficiently similar, the result images look very realistic. With the
automation of the image segmentation the pipeline becomes completely
independent from any user interaction, which allows for new applications.
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving
Authors: Ganesh Sistu, Isabelle Leang, Senthil Yogamani
Date: 2019-01-12 00:00:00
Tasks: Decoder, Depth Estimation, Object, object-detection, Object Detection, Optical Flow Estimation, Segmentation, Semantic Segmentation

Convolutional Neural Networks (CNN) are successfully used for various visual
perception tasks including bounding box object detection, semantic
segmentation, optical flow, depth estimation and visual SLAM. Generally these
tasks are independently explored and modeled. In this paper, we present a joint
multi-task network design for learning object detection and semantic
segmentation simultaneously. The main motivation is to achieve real-time
performance on a low power embedded SOC by sharing of encoder for both the
tasks. We construct an efficient architecture using a small ResNet10 like
encoder which is shared for both decoders. Object detection uses YOLO v2 like
decoder and semantic segmentation uses FCN8 like decoder. We evaluate the
proposed network in two public datasets (KITTI, Cityscapes) and in our private
fisheye camera dataset, and demonstrate that joint network provides the same
accuracy as that of separate networks. We further optimize the network to
achieve 30 fps for 1280x384 resolution image.
NRMVS: Non-Rigid Multi-View Stereo
Authors: Matthias Innmann, Kihwan Kim, Jinwei Gu, Matthias Niessner, Charles Loop, Marc Stamminger, Jan Kautz
Date: 2019-01-12 00:00:00
Tasks: 3D Reconstruction, Depth Estimation

Scene reconstruction from unorganized RGB images is an important task in many
computer vision applications. Multi-view Stereo (MVS) is a common solution in
photogrammetry applications for the dense reconstruction of a static scene. The
static scene assumption, however, limits the general applicability of MVS
algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes,
faces, or human bodies. In this paper, we open up a new challenging direction:
dense 3D reconstruction of scenes with non-rigid changes observed from
arbitrary, sparse, and wide-baseline views. We formulate the problem as a joint
optimization of deformation and depth estimation, using deformation graphs as
the underlying representation. We propose a new sparse 3D to 2D matching
technique, together with a dense patch-match evaluation scheme to estimate
deformation and depth with photometric consistency. We show that creating a
dense 4D structure from a few RGB images with non-rigid changes is possible,
and demonstrate that our method can be used to interpolate novel deformed
scenes from various combinations of these deformation estimates derived from
the sparse views.
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit
Authors: Jascha Sohl-Dickstein, Kenji Kawaguchi
Date: 2019-01-12 00:00:00
Tasks: All

Recent work has noted that all bad local minima can be removed from neural
network loss landscapes, by adding a single unit with a particular
parameterization. We show that the core technique from these papers can be used
to remove all bad local minima from any loss landscape, so long as the global
minimum has a loss of zero. This procedure does not require the addition of
auxiliary units, or even that the loss be associated with a neural network. The
method of action involves all bad local minima being converted into bad
(non-local) minima at infinity in terms of auxiliary parameters.
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data
Authors: Alexander G. Rakowski, Petar Veličković, Enrico Dall'Ara, Pietro Liò
Date: 2019-01-12 00:00:00
Tasks: General Classification

ChronoMID builds on the success of cross-modal convolutional neural networks
(X-CNNs), making the novel application of the technique to medical imaging
data. Specifically, this paper presents and compares alternative approaches -
timestamps and difference images - to incorporate temporal information for the
classification of bone disease in mice, applied to micro-CT scans of mouse
tibiae. Whilst much previous work on diseases and disease classification has
been based on mathematical models incorporating domain expertise and the
explicit encoding of assumptions, the approaches given here utilise the growing
availability of computing resources to analyse large datasets and uncover
subtle patterns in both space and time. After training on a balanced set of
over 75000 images, all models incorporating temporal features outperformed a
state-of-the-art CNN baseline on an unseen, balanced validation set comprising
over 20000 images. The top-performing model achieved 99.54% accuracy, compared
to 73.02% for the CNN baseline.
Are Clusterings of Multiple Data Views Independent?
Authors: Lucy L. Gao, Jacob Bien, Daniela Witten
Date: 2019-01-12 00:00:00
Tasks: Clustering

In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple
types of data are collected on a single set of healthy participants at multiple
timepoints in order to characterize and optimize wellness. One way to do this
is to identify clusters, or subgroups, among the participants, and then to
tailor personalized health recommendations to each subgroup. It is tempting to
cluster the participants using all of the data types and timepoints, in order
to fully exploit the available information. However, clustering the
participants based on multiple data views implicitly assumes that a single
underlying clustering of the participants is shared across all data views. If
this assumption does not hold, then clustering the participants using multiple
data views may lead to spurious results. In this paper, we seek to evaluate the
assumption that there is some underlying relationship among the clusterings
from the different data views, by asking the question: are the clusters within
each data view dependent or independent? We develop a new test for answering
this question, which we then apply to clinical, proteomic, and metabolomic
data, across two distinct timepoints, from the P100 study. We find that while
the subgroups of the participants defined with respect to any single data type
seem to be dependent across time, the clustering among the participants based
on one data type (e.g. proteomic data) appears not to be associated with the
clustering based on another data type (e.g. clinical data).
A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors
Authors: Zoleikha Jahanbakhsh-Nagadeh, Mohammad-Reza Feizi-Derakhshi, Arash Sharifi
Date: 2019-01-12 00:00:00
Tasks: General Classification, Rumour Detection

Speech Acts (SAs) are one of the important areas of pragmatics, which give us a better understanding of the state of mind of the people and convey an intended language function. Knowledge of the SA of a text can be helpful in analyzing that text in natural language processing applications. This study presents a dictionary-based statistical technique for Persian SA recognition. The proposed technique classifies a text into seven classes of SA based on four criteria: lexical, syntactic, semantic, and surface features. WordNet as the tool for extracting synonym and enriching features dictionary is utilized. To evaluate the proposed technique, we utilized four classification methods including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN). The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0.95 for classification of Persian SAs. Our original vision of this work is introducing an application of SA recognition on social media content, especially the common SA in rumors. Therefore, the proposed system utilized to determine the common SAs in rumors. The results showed that Persian rumors are often expressed in three SA classes including narrative, question, and threat, and in some cases with the request SA.
Recombination of Artificial Neural Networks
Authors: Aaron Vose, Jacob Balma, Alex Heye, Alessandro Rigazzi, Charles Siegel, Diana Moise, Benjamin Robbins, Rangan Sukumar
Date: 2019-01-12 00:00:00
Tasks: Hyperparameter Optimization

We propose a genetic algorithm (GA) for hyperparameter optimization of
artificial neural networks which includes chromosomal crossover as well as a
decoupling of parameters (i.e., weights and biases) from hyperparameters (e.g.,
learning rate, weight decay, and dropout) during sexual reproduction. Children
are produced from three parents; two contributing hyperparameters and one
contributing the parameters. Our version of population-based training (PBT)
combines traditional gradient-based approaches such as stochastic gradient
descent (SGD) with our GA to optimize both parameters and hyperparameters
across SGD epochs. Our improvements over traditional PBT provide an increased
speed of adaptation and a greater ability to shed deleterious genes from the
population. Our methods improve final accuracy as well as time to fixed
accuracy on a wide range of deep neural network architectures including
convolutional neural networks, recurrent neural networks, dense neural
networks, and capsule networks.
Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods
Authors: Samuel Li, Talayeh Razzaghi
Date: 2019-01-12 00:00:00
Tasks: BIG-bench Machine Learning, Feature Importance, General Classification, imbalanced classification, Prediction

In this work, we investigate the importance of ethnicity in colorectal cancer
survivability prediction using machine learning techniques and the SEER cancer
incidence database. We compare model performances for 2-year survivability
prediction and feature importance rankings between Hispanic, White, and mixed
patient populations. Our models consistently perform better on single-ethnicity
populations and provide different feature importance rankings when trained in
different populations. Additionally, we show our models achieve higher Area
Under Curve (AUC) score than the best reported in the literature. We also apply
imbalanced classification techniques to improve classification performance when
the number of patients who have survived from colorectal cancer is much larger
than who have not. These results provide evidence in favor for increased
consideration of patient ethnicity in cancer survivability prediction, and for
more personalized medicine in general.
Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
Authors: Brian Gaudet, Richard Linares, Roberto Furfaro
Date: 2019-01-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a novel predictive model architecture based on the principles of
predictive coding that enables open loop prediction of future observations over
extended horizons. There are two key innovations. First, whereas current
methods typically learn to make long-horizon open-loop predictions using a
multi-step cost function, we instead run the model open loop in the forward
pass during training. Second, current predictive coding models initialize the
representation layer's hidden state to a constant value at the start of an
episode, and consequently typically require multiple steps of interaction with
the environment before the model begins to produce accurate predictions.
Instead, we learn a mapping from the first observation in an episode to the
hidden state, allowing the trained model to immediately produce accurate
predictions. We compare the performance of our architecture to a standard
predictive coding model and demonstrate the ability of the model to make
accurate long horizon open-loop predictions of simulated Doppler radar
altimeter readings during a six degree of freedom Mars landing. Finally, we
demonstrate a 2X reduction in sample complexity by using the model to implement
a Dyna style algorithm to accelerate policy learning with proximal policy
optimization.
SteganoGAN: High Capacity Image Steganography with GANs
Authors: Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, Kalyan Veeramachaneni
Date: 2019-01-12 00:00:00
Tasks: Image Steganography, Steganalysis, Vocal Bursts Intensity Prediction

Image steganography is a procedure for hiding messages inside pictures. While
other techniques such as cryptography aim to prevent adversaries from reading
the secret message, steganography aims to hide the presence of the message
itself. In this paper, we propose a novel technique for hiding arbitrary binary
data in images using generative adversarial networks which allow us to optimize
the perceptual quality of the images produced by our model. We show that our
approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades
detection by steganalysis tools, and is effective on images from multiple
datasets. To enable fair comparisons, we have released an open source library
that is available online at https://github.com/DAI-Lab/SteganoGAN.