pwc-archive/papers-with-abstracts huggingface dataset:
Training and Testing Object Detectors with Virtual Images
Authors: Yonglin Tian, Xuan Li, Kunfeng Wang, Fei-Yue Wang
Date: 2017-12-22 00:00:00
Tasks: Object

In the area of computer vision, deep learning has produced a variety of
state-of-the-art models that rely on massive labeled data. However, collecting
and annotating images from the real world has a great demand for labor and
money investments and is usually too passive to build datasets with specific
characteristics, such as small area of objects and high occlusion level. Under
the framework of Parallel Vision, this paper presents a purposeful way to
design artificial scenes and automatically generate virtual images with precise
annotations. A virtual dataset named ParallelEye is built, which can be used
for several computer vision tasks. Then, by training the DPM (Deformable Parts
Model) and Faster R-CNN detectors, we prove that the performance of models can
be significantly improved by combining ParallelEye with publicly available
real-world datasets during the training phase. In addition, we investigate the
potential of testing the trained models from a specific aspect using
intentionally designed virtual datasets, in order to discover the flaws of
trained models. From the experimental results, we conclude that our virtual
dataset is viable to train and test the object detectors.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Authors: Lorenz Berger, Eoin Hyde, M. Jorge Cardoso, Sebastien Ourselin
Date: 2017-09-08 00:00:00
Tasks: Anatomy, Object Recognition, Segmentation, Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in
object recognition tasks and dense classification problems such as semantic
segmentation. However, training deep neural networks on large and sparse
datasets is still challenging and can require large amounts of computation and
memory. In this work, we address the task of performing semantic segmentation
on large data sets, such as three-dimensional medical images. We propose an
adaptive sampling scheme that uses a-posterior error maps, generated throughout
training, to focus sampling on difficult regions, resulting in improved
learning. Our contribution is threefold: 1) We give a detailed description of
the proposed sampling algorithm to speed up and improve learning performance on
large images. We propose a deep dual path CNN that captures information at fine
and coarse scales, resulting in a network with a large field of view and high
resolution outputs. We show that our method is able to attain new
state-of-the-art results on the VISCERAL Anatomy benchmark.
Neural Networks Regularization Through Class-wise Invariant Representation Learning
Authors: Soufiane Belharbi, Clément Chatelain, Romain Hérault, Sébastien Adam
Date: 2017-09-06 00:00:00
Tasks: General Classification, Representation Learning

Training deep neural networks is known to require a large number of training
samples. However, in many applications only few training samples are available.
In this work, we tackle the issue of training neural networks for
classification task when few training samples are available. We attempt to
solve this issue by proposing a new regularization term that constrains the
hidden layers of a network to learn class-wise invariant representations. In
our regularization framework, learning invariant representations is generalized
to the class membership where samples with the same class should have the same
representation. Numerical experiments over MNIST and its variants showed that
our proposal helps improving the generalization of neural network particularly
when trained with few samples. We provide the source code of our framework
https://github.com/sbelharbi/learning-class-invariant-features .
Cross-Validation with Confidence
Authors: Jing Lei
Date: 2017-03-23 00:00:00
Tasks: Model Selection, Variable Selection

Cross-validation is one of the most popular model selection methods in
statistics and machine learning. Despite its wide applicability, traditional
cross validation methods tend to select overfitting models, due to the
ignorance of the uncertainty in the testing sample. We develop a new,
statistically principled inference tool based on cross-validation that takes
into account the uncertainty in the testing sample. This new method outputs a
set of highly competitive candidate models containing the best one with
guaranteed probability. As a consequence, our method can achieve consistent
variable selection in a classical linear regression setting, for which existing
cross-validation methods require unconventional split ratios. When used for
regularizing tuning parameter selection, the method can provide a further
trade-off between prediction accuracy and model interpretability. We
demonstrate the performance of the proposed method in several simulated and
real data examples.
True Asymptotic Natural Gradient Optimization
Authors: Yann Ollivier
Date: 2017-12-22 00:00:00

We introduce a simple algorithm, True Asymptotic Natural Gradient
Optimization (TANGO), that converges to a true natural gradient descent in the
limit of small learning rates, without explicit Fisher matrix estimation.
  For quadratic models the algorithm is also an instance of averaged stochastic
gradient, where the parameter is a moving average of a "fast", constant-rate
gradient descent. TANGO appears as a particular de-linearization of averaged
SGD, and is sometimes quite different on non-quadratic models. This further
connects averaged SGD and natural gradient, both of which are arguably optimal
asymptotically.
  In large dimension, small learning rates will be required to approximate the
natural gradient well. Still, this shows it is possible to get arbitrarily
close to exact natural gradient descent with a lightweight algorithm.
Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures
Authors: Andre Mastmeyer, Matthias Wilms, Heinz Handels
Date: 2017-12-05 00:00:00

Virtual reality (VR) training simulators of liver needle insertion in the
hepatic area of breathing virtual patients currently need 4D data acquisitions
as a prerequisite. Here, first a population-based breathing virtual patient 4D
atlas can be built and second the requirement of a dose-relevant or expensive
acquisition of a 4D data set for a new static 3D patient can be mitigated by
warping the mean atlas motion. The breakthrough contribution of this work is
the construction and reuse of population-based learned 4D motion models.
Inverse Classification for Comparison-based Interpretability in Machine Learning
Authors: Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki
Date: 2017-12-22 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

In the context of post-hoc interpretability, this paper addresses the task of
explaining the prediction of a classifier, considering the case where no
information is available, neither on the classifier itself, nor on the
processed data (neither the training nor the test data). It proposes an
instance-based approach whose principle consists in determining the minimal
changes needed to alter a prediction: given a data point whose classification
must be explained, the proposed method consists in identifying a close
neighbour classified differently, where the closeness definition integrates a
sparsity constraint. This principle is implemented using observation generation
in the Growing Spheres algorithm. Experimental results on two datasets
illustrate the relevance of the proposed approach that can be used to gain
knowledge about the classifier.
Novel Ranking-Based Lexical Similarity Measure for Word Embedding
Authors: Jakub Dutkiewicz, Czesław Jędrzejek
Date: 2017-12-22 00:00:00
Tasks: Information Retrieval, Retrieval

Distributional semantics models derive word space from linguistic items in
context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. In
this paper we provide a guideline for post process improvements to the baseline
vectors. We focus on refining the similarity aspect, address imperfections of
the model by applying the hubness reduction method, implementing relational
knowledge into the model, and providing a new ranking similarity definition
that give maximum weight to the top 1 component value. This feature ranking is
similar to the one used in information retrieval. All these enrichments
outperform current literature results for joint ESL and TOEF sets comparison.
Since single word embedding is a basic element of any semantic task one can
expect a significant improvement of results for these tasks. Moreover, our
improved method of text processing can be translated to continuous distributed
representation of biological sequences for deep proteomics and genomics.
Taking Visual Motion Prediction To New Heightfields
Authors: Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi
Date: 2017-12-22 00:00:00
Tasks: motion prediction, Prediction

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and estimating the associated parameters. In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortunately, such approaches are unsuited for modeling complex real-world scenarios, where manually authoring relevant state spaces tend to be tedious and challenging. In this work, we investigate if neural networks can implicitly learn physical states of real-world mechanical processes only based on visual data while internally modeling non-homogeneous environment and in the process enable long-term physical extrapolation. We develop a recurrent neural network architecture for this task and also characterize resultant uncertainties in the form of evolving variance estimates. We evaluate our setup to extrapolate motion of rolling ball(s) on bowls of varying shape and orientation, and on arbitrary heightfields using only images as input. We report significant improvements over existing image-based methods both in terms of accuracy of predictions and complexity of scenarios; and report competitive performance with approaches that, unlike us, assume access to internal physical states.
Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging - with data from the Osteoarthritis Initiative
Authors: Erik B. Dam
Date: 2017-12-22 00:00:00

Scanner drift is a well-known magnetic resonance imaging (MRI) artifact
characterized by gradual signal degradation and scan intensity changes over
time. In addition, hardware and software updates may imply abrupt changes in
signal. The combined effects are particularly challenging for automatic image
analysis methods used in longitudinal studies. The implication is increased
measurement variation and a risk of bias in the estimations (e.g. in the volume
change for a structure). We proposed two quite different approaches for scanner
drift normalization and demonstrated the performance for segmentation of knee
MRI using the fully automatic KneeIQ framework. The validation included a total
of 1975 scans from both high-field and low-field MRI. The results demonstrated
that the pre-processing method denoted Atlas Affine Normalization significantly
removed scanner drift effects and ensured that the cartilage volume change
quantifications became consistent with manual expert scores.
On the Integration of Optical Flow and Action Recognition
Authors: Laura Sevilla-Lara, Yiyi Liao, Fatma Guney, Varun Jampani, Andreas Geiger, Michael J. Black
Date: 2017-12-22 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Most of the top performing action recognition methods use optical flow as a
"black box" input. Here we take a deeper look at the combination of flow and
action recognition, and investigate why optical flow is helpful, what makes a
flow method good for action recognition, and how we can make it better. In
particular, we investigate the impact of different flow algorithms and input
transformations to better understand how these affect a state-of-the-art action
recognition method. Furthermore, we fine tune two neural-network flow methods
end-to-end on the most widely used action recognition dataset (UCF101). Based
on these experiments, we make the following five observations: 1) optical flow
is useful for action recognition because it is invariant to appearance, 2)
optical flow methods are optimized to minimize end-point-error (EPE), but the
EPE of current methods is not well correlated with action recognition
performance, 3) for the flow methods tested, accuracy at boundaries and at
small displacements is most correlated with action recognition performance, 4)
training optical flow to minimize classification error instead of minimizing
EPE improves recognition performance, and 5) optical flow learned for the task
of action recognition differs from traditional optical flow especially inside
the human body and at the boundary of the body. These observations may
encourage optical flow researchers to look beyond EPE as a goal and guide
action recognition researchers to seek better motion cues, leading to a tighter
integration of the optical flow and action recognition communities.
Disentangled Representations for Manipulation of Sentiment in Text
Authors: Maria Larsson, Amanda Nilsson, Mikael Kågebäck
Date: 2017-12-22 00:00:00
Tasks: Marketing

The ability to change arbitrary aspects of a text while leaving the core
message intact could have a strong impact in fields like marketing and politics
by enabling e.g. automatic optimization of message impact and personalized
language adapted to the receiver's profile. In this paper we take a first step
towards such a system by presenting an algorithm that can manipulate the
sentiment of a text while preserving its semantics using disentangled
representations. Validation is performed by examining trajectories in embedding
space and analyzing transformed sentences for semantic preservation while
expression of desired sentiment shift.
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research
Authors: Xuan Li, Kunfeng Wang, Yonglin Tian, Lan Yan, Fei-Yue Wang
Date: 2017-12-22 00:00:00
Tasks: Instance Segmentation, Object Tracking, Optical Flow Estimation, Semantic Segmentation

Video image datasets are playing an essential role in design and evaluation
of traffic vision algorithms. Nevertheless, a longstanding inconvenience
concerning image datasets is that manually collecting and annotating
large-scale diversified datasets from real scenes is time-consuming and prone
to error. For that virtual datasets have begun to function as a proxy of real
datasets. In this paper, we propose to construct large-scale artificial scenes
for traffic vision research and generate a new virtual dataset called
"ParallelEye". First of all, the street map data is used to build 3D scene
model of Zhongguancun Area, Beijing. Then, the computer graphics, virtual
reality, and rule modeling technologies are utilized to synthesize large-scale,
realistic virtual urban traffic scenes, in which the fidelity and geography
match the real world well. Furthermore, the Unity3D platform is used to render
the artificial scenes and generate accurate ground-truth labels, e.g.,
semantic/instance segmentation, object bounding box, object tracking, optical
flow, and depth. The environmental conditions in artificial scenes can be
controlled completely. As a result, we present a viable implementation pipeline
for constructing large-scale artificial scenes for traffic vision research. The
experimental results demonstrate that this pipeline is able to generate
photorealistic virtual datasets with low modeling time and high accuracy
labeling.
Boosted Generative Models
Authors: Aditya Grover, Stefano Ermon
Date: 2017-02-27 00:00:00
Tasks: Density Estimation, General Classification

We propose a novel approach for using unsupervised boosting to create an
ensemble of generative models, where models are trained in sequence to correct
earlier mistakes. Our meta-algorithmic framework can leverage any existing base
learner that permits likelihood evaluation, including recent deep expressive
models. Further, our approach allows the ensemble to include discriminative
models trained to distinguish real data from model-generated data. We show
theoretical conditions under which incorporating a new model in the ensemble
will improve the fit and empirically demonstrate the effectiveness of our
black-box boosting algorithms on density estimation, classification, and sample
generation on benchmark datasets for a wide range of generative models.
Action Schema Networks: Generalised Policies with Deep Learning
Authors: Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie
Date: 2017-09-13 00:00:00
Tasks: Deep Learning

In this paper, we introduce the Action Schema Network (ASNet): a neural
network architecture for learning generalised policies for probabilistic
planning problems. By mimicking the relational structure of planning problems,
ASNets are able to adopt a weight-sharing scheme which allows the network to be
applied to any problem from a given planning domain. This allows the cost of
training the network to be amortised over all problems in that domain. Further,
we propose a training method which balances exploration and supervised training
on small problems to produce a policy which remains robust when evaluated on
larger problems. In experiments, we show that ASNet's learning capability
allows it to significantly outperform traditional non-learning planners in
several challenging domains.
Towards dense object tracking in a 2D honeybee hive
Authors: Katarzyna Bozek, Laetitia Hebert, Alexander S Mikheyev, Greg J. Stephens
Date: 2017-12-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Object Detection, Object Tracking, Semantic Segmentation, Time Series Analysis

From human crowds to cells in tissue, the detection and efficient tracking of
multiple objects in dense configurations is an important and unsolved problem.
In the past, limitations of image analysis have restricted studies of dense
groups to tracking a single or subset of marked individuals, or to
coarse-grained group-level dynamics, all of which yield incomplete information.
Here, we combine convolutional neural networks (CNNs) with the model
environment of a honeybee hive to automatically recognize all individuals in a
dense group from raw image data. We create new, adapted individual labeling and
use the segmentation architecture U-Net with a loss function dependent on both
object identity and orientation. We additionally exploit temporal regularities
of the video recording in a recurrent manner and achieve near human-level
performance while reducing the network size by 94% compared to the original
U-Net architecture. Given our novel application of CNNs, we generate extensive
problem-specific image data in which labeled examples are produced through a
custom interface with Amazon Mechanical Turk. This dataset contains over
375,000 labeled bee instances across 720 video frames at 2 FPS, representing an
extensive resource for the development and testing of tracking methods. We
correctly detect 96% of individuals with a location error of ~7% of a typical
body dimension, and orientation error of 12 degrees, approximating the
variability of human raters. Our results provide an important step towards
efficient image-based dense object tracking by allowing for the accurate
determination of object location and orientation across time-series image data
efficiently within one network architecture.
Virtual Sensor Modelling using Neural Networks with Coefficient-based Adaptive Weights and Biases Search Algorithm for Diesel Engines
Authors: Kushagra Rastogi, Navreet Saini
Date: 2017-12-22 00:00:00

With the explosion in the field of Big Data and introduction of more
stringent emission norms every three to five years, automotive companies must
not only continue to enhance the fuel economy ratings of their products, but
also provide valued services to their customers such as delivering engine
performance and health reports at regular intervals. A reasonable solution to
both issues is installing a variety of sensors on the engine. Sensor data can
be used to develop fuel economy features and will directly indicate engine
performance. However, mounting a plethora of sensors is impractical in a very
cost-sensitive industry. Thus, virtual sensors can replace physical sensors by
reducing cost while capturing essential engine data.
Runaway Feedback Loops in Predictive Policing
Authors: Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, Suresh Venkatasubramanian
Date: 2017-06-29 00:00:00

Predictive policing systems are increasingly used to determine how to
allocate police across a city in order to best prevent crime. Discovered crime
data (e.g., arrest counts) are used to help update the model, and the process
is repeated. Such systems have been empirically shown to be susceptible to
runaway feedback loops, where police are repeatedly sent back to the same
neighborhoods regardless of the true crime rate.
  In response, we develop a mathematical model of predictive policing that
proves why this feedback loop occurs, show empirically that this model exhibits
such problems, and demonstrate how to change the inputs to a predictive
policing system (in a black-box manner) so the runaway feedback loop does not
occur, allowing the true crime rate to be learned. Our results are
quantitative: we can establish a link (in our model) between the degree to
which runaway feedback causes problems and the disparity in crime rates between
areas. Moreover, we can also demonstrate the way in which \emph{reported}
incidents of crime (those reported by residents) and \emph{discovered}
incidents of crime (i.e. those directly observed by police officers dispatched
as a result of the predictive policing algorithm) interact: in brief, while
reported incidents can attenuate the degree of runaway feedback, they cannot
entirely remove it without the interventions we suggest.
Source-side Prediction for Neural Headline Generation
Authors: Shun Kiyono, Sho Takase, Jun Suzuki, Naoaki Okazaki, Kentaro Inui, Masaaki Nagata
Date: 2017-12-22 00:00:00
Tasks: Decoder, Headline Generation, Prediction, Text Generation

The encoder-decoder model is widely used in natural language generation
tasks. However, the model sometimes suffers from repeated redundant generation,
misses important phrases, and includes irrelevant entities. Toward solving
these problems we propose a novel source-side token prediction module. Our
method jointly estimates the probability distributions over source and target
vocabularies to capture a correspondence between source and target tokens. The
experiments show that the proposed model outperforms the current
state-of-the-art method in the headline generation task. Additionally, we show
that our method has an ability to learn a reasonable token-wise correspondence
without knowing any true alignments.