pwc-archive/papers-with-abstracts huggingface dataset:
Ridesourcing Car Detection by Transfer Learning
Authors: Leye Wang, Xu Geng, Jintao Ke, Chen Peng, Xiaojuan Ma, Daqing Zhang, Qiang Yang
Date: 2017-05-23 00:00:00
Tasks: Transfer Learning

Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate black market, we design a method to detect ridesourcing cars from
a pool of cars based on their trajectories. Since licensed ridesourcing car
traces are not openly available and may be completely missing in some cities
due to legal issues, we turn to transferring knowledge from public transport
open data, i.e, taxis and buses, to ridesourcing detection among ordinary
vehicles. We propose a two-stage transfer learning framework. In Stage 1, we
take taxi and bus data as input to learn a random forest (RF) classifier using
trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we
use the RF to label all the candidate cars. In Stage 2, leveraging the subset
of high confident labels from the previous stage as input, we further learn a
convolutional neural network (CNN) classifier for ridesourcing detection, and
iteratively refine RF and CNN, as well as the feature set, via a co-training
process. Finally, we use the resulting ensemble of RF and CNN to identify the
ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus
traces show that our transfer learning framework, with no need of a pre-labeled
ridesourcing dataset, can achieve similar accuracy as the supervised learning
methods.
AI, Native Supercomputing and The Revival of Moore's Law
Authors: Chien-Ping Lu
Date: 2017-05-17 00:00:00
Tasks: GPU

Based on Alan Turing's proposition on AI and computing machinery, which
shaped Computing as we know it today, the new AI computing machinery should
comprise a universal computer and a universal learning machine. The later
should understand linear algebra natively to overcome the slowdown of Moore's
law. In such a universal learnig machine, a computing unit does not need to
keep the legacy of a universal computing core. The data can be distributed to
the computing units, and the results can be collected from them through
Collective Streaming, reminiscent of Collective Communication in
Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy,
nor a TPU-like fine-grain mesh.
Continual Learning in Generative Adversarial Nets
Authors: Ari Seff, Alex Beatson, Daniel Suo, Han Liu
Date: 2017-05-23 00:00:00
Tasks: Continual Learning

Developments in deep generative models have allowed for tractable learning of
high-dimensional data distributions. While the employed learning procedures
typically assume that training data is drawn i.i.d. from the distribution of
interest, it may be desirable to model distinct distributions which are
observed sequentially, such as when different classes are encountered over
time. Although conditional variations of deep generative models permit multiple
distributions to be modeled by a single network in a disentangled fashion, they
are susceptible to catastrophic forgetting when the distributions are
encountered sequentially. In this paper, we adapt recent work in reducing
catastrophic forgetting to the task of training generative adversarial networks
on a sequence of distinct distributions, enabling continual generative
modeling.
Exponential error rates of SDP for block models: Beyond Grothendieck's inequality
Authors: Yingjie Fei, Yudong Chen
Date: 2017-05-23 00:00:00
Tasks: Stochastic Block Model

In this paper we consider the cluster estimation problem under the Stochastic
Block Model. We show that the semidefinite programming (SDP) formulation for
this problem achieves an error rate that decays exponentially in the
signal-to-noise ratio. The error bound implies weak recovery in the sparse
graph regime with bounded expected degrees, as well as exact recovery in the
dense regime. An immediate corollary of our results yields error bounds under
the Censored Block Model. Moreover, these error bounds are robust, continuing
to hold under heterogeneous edge probabilities and a form of the so-called
monotone attack.
  Significantly, this error rate is achieved by the SDP solution itself without
any further pre- or post-processing, and improves upon existing
polynomially-decaying error bounds proved using the Grothendieck\textquoteright
s inequality. Our analysis has two key ingredients: (i) showing that the graph
has a well-behaved spectrum, even in the sparse regime, after discounting an
exponentially small number of edges, and (ii) an order-statistics argument that
governs the final error rate. Both arguments highlight the implicit
regularization effect of the SDP formulation.
Classification of Aerial Photogrammetric 3D Point Clouds
Authors: Carlos Becker, Nicolai Häni, Elena Rosinskaya, Emmanuel d'Angelo, Christoph Strecha
Date: 2017-05-23 00:00:00
Tasks: Classification, General Classification, Point Cloud Classification, Scene Understanding

We present a powerful method to extract per-point semantic class labels from
aerialphotogrammetry data. Labeling this kind of data is important for tasks
such as environmental modelling, object classification and scene understanding.
Unlike previous point cloud classification methods that rely exclusively on
geometric features, we show that incorporating color information yields a
significant increase in accuracy in detecting semantic classes. We test our
classification method on three real-world photogrammetry datasets that were
generated with Pix4Dmapper Pro, and with varying point densities. We show that
off-the-shelf machine learning techniques coupled with our new features allow
us to train highly accurate classifiers that generalize well to unseen data,
processing point clouds containing 10 million points in less than 3 minutes on
a desktop computer.
Kernel Truncated Regression Representation for Robust Subspace Clustering
Authors: Liangli Zhen, Dezhong Peng, Wei Wang, Xin Yao
Date: 2017-05-15 00:00:00
Tasks: Clustering, regression

Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.
Explaining Transition Systems through Program Induction
Authors: Svetlin Penkov, Subramanian Ramamoorthy
Date: 2017-05-23 00:00:00
Tasks: Program induction

Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to three problems: system
identification of dynamical systems, explaining the behaviour of a DQN agent
and learning by demonstration in a human-robot interaction scenario. Our
experimental results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
Authors: Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou, Yun Zhou, Zakaria Mhammedi, Stephen O'Leary, James Bailey
Date: 2017-03-04 00:00:00

Simulation-based training (SBT) is gaining popularity as a low-cost and
convenient training technique in a vast range of applications. However, for a
SBT platform to be fully utilized as an effective training tool, it is
essential that feedback on performance is provided automatically in real-time
during training. It is the aim of this paper to develop an efficient and
effective feedback generation method for the provision of real-time feedback in
SBT. Existing methods either have low effectiveness in improving novice skills
or suffer from low efficiency, resulting in their inability to be used in
real-time. In this paper, we propose a neural network based method to generate
feedback using the adversarial technique. The proposed method utilizes a
bounded adversarial update to minimize a L1 regularized loss via
back-propagation. We empirically show that the proposed method can be used to
generate simple, yet effective feedback. Also, it was observed to have high
effectiveness and efficiency when compared to existing methods, thus making it
a promising option for real-time feedback generation in SBT.
How hard can it be? Estimating the difficulty of visual search in an image
Authors: Radu Tudor Ionescu, Bogdan Alexe, Marius Leordeanu, Marius Popescu, Dim P. Papadopoulos, Vittorio Ferrari
Date: 2017-05-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object Localization, Weakly-Supervised Object Localization

We address the problem of estimating image difficulty defined as the human
response time for solving a visual search task. We collect human annotations of
image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing
platform. We then analyze what human interpretable image properties can have an
impact on visual search difficulty, and how accurate are those properties for
predicting difficulty. Next, we build a regression model based on deep features
learned with state of the art convolutional neural networks and show better
results for predicting the ground-truth visual search difficulty scores
produced by human annotators. Our model is able to correctly rank about 75%
image pairs according to their difficulty score. We also show that our
difficulty predictor generalizes well to new classes not seen during training.
Finally, we demonstrate that our predicted difficulty scores are useful for
weakly supervised object localization (8% improvement) and semi-supervised
object classification (1% improvement).
Geometric Loss Functions for Camera Pose Regression with Deep Learning
Authors: Alex Kendall, Roberto Cipolla
Date: 2017-04-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deep Learning, Position, regression

Deep learning has shown to be effective for robust and real-time monocular
image relocalisation. In particular, PoseNet is a deep convolutional neural
network which learns to regress the 6-DOF camera pose from a single image. It
learns to localize using high level features and is robust to difficult
lighting, motion blur and unknown camera intrinsics, where point based SIFT
registration fails. However, it was trained using a naive loss function, with
hyper-parameters which require expensive tuning. In this paper, we give the
problem a more fundamental theoretical treatment. We explore a number of novel
loss functions for learning camera pose which are based on geometry and scene
reprojection error. Additionally we show how to automatically learn an optimal
weighting to simultaneously regress position and orientation. By leveraging
geometry, we demonstrate that our technique significantly improves PoseNet's
performance across datasets ranging from indoor rooms to a small city.
MRI-PET Registration with Automated Algorithm in Pre-clinical Studies
Authors: Nathanael L. Baisa, Stéphanie Bricq, Alain Lalande
Date: 2017-05-19 00:00:00

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
automatic 3-D registration is implemented and validated for small animal image
volumes so that the high-resolution anatomical MRI information can be fused
with the low spatial resolution of functional PET information for the
localization of lesion that is currently in high demand in the study of tumor
of cancer (oncology) and its corresponding preparation of pharmaceutical drugs.
Though many registration algorithms are developed and applied on human brain
volumes, these methods may not be as efficient on small animal datasets due to
lack of intensity information and often the high anisotropy in voxel
dimensions. Therefore, a fully automatic registration algorithm which can
register not only assumably rigid small animal volumes such as brain but also
deformable organs such as kidney, cardiac and chest is developed using a
combination of global affine and local B-spline transformation models in which
mutual information is used as a similarity criterion. The global affine
registration uses a multi-resolution pyramid on image volumes of 3 levels
whereas in local B-spline registration, a multi-resolution scheme is applied on
the B-spline grid of 2 levels on the finest resolution of the image volumes in
which only the transform itself is affected rather than the image volumes.
Since mutual information lacks sufficient spatial information, PCA is used to
inject it by estimating initial translation and rotation parameters. It is
computationally efficient since it is implemented using C++ and ITK library,
and is qualitatively and quantitatively shown that this PCA-initialized global
registration followed by local registration is in close agreement with expert
manual registration and outperforms the one without PCA initialization tested
on small animal brain and kidney.
Shake-Shake regularization
Authors: Xavier Gastaldi
Date: 2017-05-21 00:00:00

The method introduced in this paper aims at helping deep learning
practitioners faced with an overfit problem. The idea is to replace, in a
multi-branch network, the standard summation of parallel branches with a
stochastic affine combination. Applied to 3-branch residual networks,
shake-shake regularization improves on the best single shot published results
on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%.
Experiments on architectures without skip connections or Batch Normalization
show encouraging results and open the door to a large set of applications. Code
is available at https://github.com/xgastaldi/shake-shake
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
Authors: Adrià Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana
Date: 2017-05-23 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

This paper analyzes the use of 3D Convolutional Neural Networks for brain
tumor segmentation in MR images. We address the problem using three different
architectures that combine fine and coarse features to obtain the final
segmentation. We compare three different networks that use multi-resolution
features in terms of both design and performance and we show that they improve
their single-resolution counterparts.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
Authors: Francesco Ciompi, Kaman Chung, Sarah J. van Riel, Arnaud Arindra Adiyoso Setio, Paul K. Gerke, Colin Jacobs, Ernst Th. Scholten, Cornelia Schaefer-Prokop, Mathilde M. W. Wille, Alfonso Marchiano, Ugo Pastorino, Mathias Prokop, Bram van Ginneken
Date: 2016-10-28 00:00:00
Tasks: Deep Learning, Lung Nodule Classification, Lung Nodule Detection, Management, Pulmonary Nodules Classification

The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size and nodule type. In this paper, we present a deep
learning system based on multi-stream multi-scale convolutional networks, which
automatically classifies all nodule types relevant for nodule workup. The
system processes raw CT data containing a nodule without the need for any
additional information such as nodule segmentation or nodule size and learns a
representation of 3D data by analyzing an arbitrary number of 2D views of a
given nodule. The deep learning system was trained with data from the Italian
MILD screening trial and validated on an independent set of data from the
Danish DLCST screening trial. We analyze the advantage of processing nodules at
multiple scales with a multi-stream convolutional network architecture, and we
show that the proposed deep learning system achieves performance at classifying
nodule type that surpasses the one of classical machine learning approaches and
is within the inter-observer variability among four experienced human
observers.
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Authors: Michael Gygli
Date: 2017-05-23 00:00:00
Tasks: Action Recognition, Boundary Detection, Camera shot boundary detection, Temporal Action Localization

Shot boundary detection (SBD) is an important component of many video
analysis tasks, such as action recognition, video indexing, summarization and
editing. Previous work typically used a combination of low-level features like
color histograms, in conjunction with simple models such as SVMs. Instead, we
propose to learn shot detection end-to-end, from pixels to final shot
boundaries. For training such a model, we rely on our insight that all shot
boundaries are generated. Thus, we create a dataset with one million frames and
automatically generated transitions such as cuts, dissolves and fades. In order
to efficiently analyze hours of videos, we propose a Convolutional Neural
Network (CNN) which is fully convolutional in time, thus allowing to use a
large temporal context without the need to repeatedly processing frames. With
this architecture our method obtains state-of-the-art results while running at
an unprecedented speed of more than 120x real-time.
MAGAN: Margin Adaptation for Generative Adversarial Networks
Authors: Ruohan Wang, Antoine Cully, Hyung Jin Chang, Yiannis Demiris
Date: 2017-04-12 00:00:00
Tasks: Image Generation

We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
algorithm, a novel training procedure for GANs to improve stability and
performance by using an adaptive hinge loss function. We estimate the
appropriate hinge loss margin with the expected energy of the target
distribution, and derive principled criteria for when to update the margin. We
prove that our method converges to its global optimum under certain
assumptions. Evaluated on the task of unsupervised image generation, the
proposed training procedure is simple yet robust on a diverse set of data, and
achieves qualitative and quantitative improvements compared to the
state-of-the-art.
The importance of stain normalization in colorectal tissue classification with convolutional networks
Authors: Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, Jeroen van der Laak
Date: 2017-02-20 00:00:00
Tasks: Classification, General Classification

The development of reliable imaging biomarkers for the analysis of colorectal
cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images
requires an accurate and reproducible classification of the main tissue
components in the image. In this paper, we propose a system for CRC tissue
classification based on convolutional networks (ConvNets). We investigate the
importance of stain normalization in tissue classification of CRC tissue
samples in H&E-stained images. Furthermore, we report the performance of
ConvNets on a cohort of rectal cancer samples and on an independent publicly
available dataset of colorectal H&E images.
Unbiasing Truncated Backpropagation Through Time
Authors: Corentin Tallec, Yann Ollivier
Date: 2017-05-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modelling

Truncated Backpropagation Through Time (truncated BPTT) is a widespread
method for learning recurrent computational graphs. Truncated BPTT keeps the
computational benefits of Backpropagation Through Time (BPTT) while relieving
the need for a complete backtrack through the whole data sequence at every
step. However, truncation favors short-term dependencies: the gradient estimate
of truncated BPTT is biased, so that it does not benefit from the convergence
guarantees from stochastic gradient theory. We introduce Anticipated Reweighted
Truncated Backpropagation (ARTBP), an algorithm that keeps the computational
benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using
variable truncation lengths together with carefully chosen compensation factors
in the backpropagation equation. We check the viability of ARTBP on two tasks.
First, a simple synthetic task where careful balancing of temporal dependencies
at different scales is needed: truncated BPTT displays unreliable performance,
and in worst case scenarios, divergence, while ARTBP converges reliably.
Second, on Penn Treebank character-level language modelling, ARTBP slightly
outperforms truncated BPTT.
Salient Object Detection with Semantic Priors
Authors: Tam V. Nguyen, Luoqi Liu
Date: 2017-05-23 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation

Salient object detection has increasingly become a popular topic in cognitive
and computational sciences, including computer vision and artificial
intelligence research. In this paper, we propose integrating \textit{semantic
priors} into the salient object detection process. Our algorithm consists of
three basic steps. Firstly, the explicit saliency map is obtained based on the
semantic segmentation refined by the explicit saliency priors learned from the
data. Next, the implicit saliency map is computed based on a trained model
which maps the implicit saliency priors embedded into regional features with
the saliency values. Finally, the explicit semantic map and the implicit map
are adaptively fused to form a pixel-accurate saliency map which uniformly
covers the objects of interest. We further evaluate the proposed framework on
two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental
results demonstrate that our method outperforms other state-of-the-art methods.