pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
Authors: Jung-Su Ha, Hyeok-Joo Chae, Han-Lim Choi
Date: 2017-11-22 00:00:00
Tasks: Motion Planning

This work presents an efficient framework to generate a motion plan of a
robot with high degrees of freedom (e.g., a humanoid robot).
High-dimensionality of the robot configuration space often leads to
difficulties in utilizing the widely-used motion planning algorithms, since the
volume of the decision space increases exponentially with the number of
dimensions. To handle complications arising from the large decision space, and
to solve a corresponding motion planning problem efficiently, two key concepts
are adopted in this work: First, the Gaussian process latent variable model
(GP-LVM) is utilized for low-dimensional representation of the original
configuration space. Second, an approximate inference algorithm is used,
exploiting through the duality between control and estimation, to explore the
decision space and to compute a high-quality motion trajectory of the robot.
Utilizing the GP-LVM and the duality between control and estimation, we
construct a fully probabilistic generative model with which a high-dimensional
motion planning problem is transformed into a tractable inference problem.
Finally, we compute the motion trajectory via an approximate inference
algorithm based on a variant of the particle filter. The resulting motions can
be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Authors: Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Wei-Nan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
Date: 2017-11-22 00:00:00
Tasks: Computational Efficiency, Graph Representation Learning, Link Prediction, Node Classification, Representation Learning

The goal of graph representation learning is to embed each vertex in a graph
into a low-dimensional vector space. Existing graph representation learning
methods can be classified into two categories: generative models that learn the
underlying connectivity distribution in the graph, and discriminative models
that predict the probability of edge existence between a pair of vertices. In
this paper, we propose GraphGAN, an innovative graph representation learning
framework unifying above two classes of methods, in which the generative model
and discriminative model play a game-theoretical minimax game. Specifically,
for a given vertex, the generative model tries to fit its underlying true
connectivity distribution over all other vertices and produces "fake" samples
to fool the discriminative model, while the discriminative model tries to
detect whether the sampled vertex is from ground truth or generated by the
generative model. With the competition between these two models, both of them
can alternately and iteratively boost their performance. Moreover, when
considering the implementation of generative model, we propose a novel graph
softmax to overcome the limitations of traditional softmax function, which can
be proven satisfying desirable properties of normalization, graph structure
awareness, and computational efficiency. Through extensive experiments on
real-world datasets, we demonstrate that GraphGAN achieves substantial gains in
a variety of applications, including link prediction, node classification, and
recommendation, over state-of-the-art baselines.
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
Authors: Yi Li, Lingxiao Song, Xiang Wu, Ran He, Tieniu Tan
Date: 2017-09-12 00:00:00
Tasks: Face Verification

Makeup is widely used to improve facial attractiveness and is well accepted
by the public. However, different makeup styles will result in significant
facial appearance changes. It remains a challenging problem to match makeup and
non-makeup face images. This paper proposes a learning from generation approach
for makeup-invariant face verification by introducing a bi-level adversarial
network (BLAN). To alleviate the negative effects from makeup, we first
generate non-makeup images from makeup ones, and then use the synthesized
non-makeup images for further verification. Two adversarial networks in BLAN
are integrated in an end-to-end deep network, with the one on pixel level for
reconstructing appealing facial images and the other on feature level for
preserving identity information. These two networks jointly reduce the sensing
gap between makeup and non-makeup images. Moreover, we make the generator well
constrained by incorporating multiple perceptual losses. Experimental results
on three benchmark makeup face datasets demonstrate that our method achieves
state-of-the-art verification accuracy across makeup status and can produce
photo-realistic non-makeup face images.
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies
Authors: Hesham M. Eraqi, Mohamed N. Moustafa, Jens Honer
Date: 2017-10-10 00:00:00
Tasks: Autonomous Vehicles

Steering a car through traffic is a complex task that is difficult to cast
into algorithms. Therefore, researchers turn to training artificial neural
networks from front-facing camera data stream along with the associated
steering angles. Nevertheless, most existing solutions consider only the visual
camera frames as input, thus ignoring the temporal relationship between frames.
In this work, we propose a Convolutional Long Short-Term Memory Recurrent
Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and
dynamic temporal dependencies of driving. Additionally, We introduce posing the
steering angle regression problem as classification while imposing a spatial
relationship between the output layer neurons. Such method is based on learning
a sinusoidal function that encodes steering angles. To train and validate our
proposed methods, we used the publicly available Comma.ai dataset. Our solution
improved steering root mean square error by 35% over recent methods, and led to
a more stable steering by 87%.
Towards a More Reliable Privacy-preserving Recommender System
Authors: Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin
Date: 2017-11-21 00:00:00
Tasks: Collaborative Filtering, Privacy Preserving, Recommendation Systems

This paper proposes a privacy-preserving distributed recommendation
framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the
privacy of value, model and existence altogether. That says, not only the
ratings from the users to the items, but also the existence of the ratings as
well as the learned recommendation model are kept private in our framework. Our
solution relies on a distributed client-server architecture and a two-stage
Randomized Response algorithm, along with an implementation on the popular
recommendation model, Matrix Factorization (MF). We further prove SDCF to meet
the guarantee of Differential Privacy so that clients are allowed to specify
arbitrary privacy levels. Experiments conducted on numerical rating prediction
and one-class rating action prediction exhibit that SDCF does not sacrifice too
much accuracy for privacy.
Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network
Authors: Yuexiang Li, Linlin Shen
Date: 2017-03-02 00:00:00
Tasks: Deep Learning, General Classification, Lesion Classification, Lesion Segmentation, Task 2

Skin lesion is a severe disease in world-wide extent. Early detection of
melanoma in dermoscopy images significantly increases the survival rate.
However, the accurate recognition of melanoma is extremely challenging due to
the following reasons, e.g. low contrast between lesions and skin, visual
similarity between melanoma and non-melanoma lesions, etc. Hence, reliable
automatic detection of skin tumors is very useful to increase the accuracy and
efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is
a challenge focusing on the automatic analysis of skin lesion. In this paper,
we proposed two deep learning methods to address all the three tasks announced
in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature
extraction (task 2) and lesion classification (task 3). A deep learning
framework consisting of two fully-convolutional residual networks (FCRN) is
proposed to simultaneously produce the segmentation result and the coarse
classification result. A lesion index calculation unit (LICU) is developed to
refine the coarse classification results by calculating the distance heat-map.
A straight-forward CNN is proposed for the dermoscopic feature extraction task.
To our best knowledges, we are not aware of any previous work proposed for this
task. The proposed deep learning frameworks were evaluated on the ISIC 2017
testing set. Experimental results show the promising accuracies of our
frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were
achieved.
TexT - Text Extractor Tool for Handwritten Document Transcription and Annotation
Authors: Anders Hast, Per Cullhed, Ekta Vats
Date: 2017-11-22 00:00:00

This paper presents a framework for semi-automatic transcription of
large-scale historical handwritten documents and proposes a simple
user-friendly text extractor tool, TexT for transcription. The proposed
approach provides a quick and easy transcription of text using computer
assisted interactive technique. The algorithm finds multiple occurrences of the
marked text on-the-fly using a word spotting system. TexT is also capable of
performing on-the-fly annotation of handwritten text with automatic generation
of ground truth labels, and dynamic adjustment and correction of user generated
bounding box annotations with the word being perfectly encapsulated. The user
can view the document and the found words in the original form or with
background noise removed for easier visualization of transcription results. The
effectiveness of TexT is demonstrated on an archival manuscript collection from
well-known publicly available dataset.
Deformable Deep Convolutional Generative Adversarial Network in Microwave Based Hand Gesture Recognition System
Authors: Jiajun Zhang, Zhiguo Shi
Date: 2017-11-06 00:00:00
Tasks: BIG-bench Machine Learning, Generative Adversarial Network, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Traditional vision-based hand gesture recognition systems is limited under
dark circumstances. In this paper, we build a hand gesture recognition system
based on microwave transceiver and deep learning algorithm. A Doppler radar
sensor with dual receiving channels at 5.8GHz is used to acquire a big database
of hand gestures signals. The received hand gesture signals are then processed
with time-frequency analysis. Based on these big databases of hand gesture, we
propose a new machine learning architecture called deformable deep
convolutional generative adversarial network. Experimental results show the new
architecture can upgrade the recognition rate by 10% and the deformable kernel
can reduce the testing time cost by 30%.
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Authors: Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
Date: 2017-11-22 00:00:00
Tasks: Deep Learning

Deep Learning models are vulnerable to adversarial examples, i.e.\ images
obtained via deliberate imperceptible perturbations, such that the model
misclassifies them with high confidence. However, class confidence by itself is
an incomplete picture of uncertainty. We therefore use principled Bayesian
methods to capture model uncertainty in prediction for observing adversarial
misclassification. We provide an extensive study with different Bayesian neural
networks attacked in both white-box and black-box setups. The behaviour of the
networks for noise, attacks and clean test data is compared. We observe that
Bayesian neural networks are uncertain in their predictions for adversarial
perturbations, a behaviour similar to the one observed for random Gaussian
perturbations. Thus, we conclude that Bayesian neural networks can be
considered for detecting adversarial examples.
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
Authors: Jiajun Zhang, Jinkun Tao, Jiangtao Huangfu, Zhiguo Shi
Date: 2017-11-07 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Hand gesture recognition has long been a hot topic in human computer
interaction. Traditional camera-based hand gesture recognition systems cannot
work properly under dark circumstances. In this paper, a Doppler Radar based
hand gesture recognition system using convolutional neural networks is
proposed. A cost-effective Doppler radar sensor with dual receiving channels at
5.8GHz is used to acquire a big database of four standard gestures. The
received hand gesture signals are then processed with time-frequency analysis.
Convolutional neural networks are used to classify different gestures.
Experimental results verify the effectiveness of the system with an accuracy of
98%. Besides, related factors such as recognition distance and gesture scale
are investigated.
Image-to-Image Translation with Conditional Adversarial Networks
Authors: Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
Date: 2016-11-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Colorization, Cross-View Image-to-Image Translation, Fundus to Angiography Generation, Image-to-Image Translation, Nuclear Segmentation, Translation

We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems that traditionally would require very different loss formulations.
We demonstrate that this approach is effective at synthesizing photos from
label maps, reconstructing objects from edge maps, and colorizing images, among
other tasks. Indeed, since the release of the pix2pix software associated with
this paper, a large number of internet users (many of them artists) have posted
their own experiments with our system, further demonstrating its wide
applicability and ease of adoption without the need for parameter tweaking. As
a community, we no longer hand-engineer our mapping functions, and this work
suggests we can achieve reasonable results without hand-engineering our loss
functions either.
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
Authors: Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer
Date: 2017-11-22 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, General Classification, Point Cloud Classification

The point cloud is gaining prominence as a method for representing 3D shapes,
but its irregular format poses a challenge for deep learning methods. The
common solution of transforming the data into a 3D voxel grid introduces its
own challenges, mainly large memory size. In this paper we propose a novel 3D
point cloud representation called 3D Modified Fisher Vectors (3DmFV). Our
representation is hybrid as it combines the discrete structure of a grid with
continuous generalization of Fisher vectors, in a compact and computationally
efficient way. Using the grid enables us to design a new CNN architecture for
point cloud classification and part segmentation. In a series of experiments we
demonstrate competitive performance or even better than state-of-the-art on
challenging benchmark datasets.
The Stochastic Firefighter Problem
Authors: Guy Tennenholtz, Constantine Caramanis, Shie Mannor
Date: 2017-11-22 00:00:00

The dynamics of infectious diseases spread is crucial in determining their
risk and offering ways to contain them. We study sequential vaccination of
individuals in networks. In the original (deterministic) version of the
Firefighter problem, a fire breaks out at some node of a given graph. At each
time step, b nodes can be protected by a firefighter and then the fire spreads
to all unprotected neighbors of the nodes on fire. The process ends when the
fire can no longer spread. We extend the Firefighter problem to a probabilistic
setting, where the infection is stochastic. We devise a simple policy that only
vaccinates neighbors of infected nodes and is optimal on regular trees and on
general graphs for a sufficiently large budget. We derive methods for
calculating upper and lower bounds of the expected number of infected
individuals, as well as provide estimates on the budget needed for containment
in expectation. We calculate these explicitly on trees, d-dimensional grids,
and Erd\H{o}s R\'{e}nyi graphs. Finally, we construct a state-dependent budget
allocation strategy and demonstrate its superiority over constant budget
allocation on real networks following a first order acquaintance vaccination
policy.
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Authors: Wenbin Li, Jeannette Bohg, Mario Fritz
Date: 2017-11-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Understanding physical phenomena is a key component of human intelligence and
enables physical interaction with previously unseen environments. In this
paper, we study how an artificial agent can autonomously acquire this intuition
through interaction with the environment. We created a synthetic block stacking
environment with physics simulation in which the agent can learn a policy
end-to-end through trial and error. Thereby, we bypass to explicitly model
physical knowledge within the policy. We are specifically interested in tasks
that require the agent to reach a given goal state that may be different for
every new trial. To this end, we propose a deep reinforcement learning
framework that learns policies which are parametrized by a goal. We validated
the model on a toy example navigating in a grid world with different target
positions and in a block stacking task with different target structures of the
final tower. In contrast to prior work, our policies show better generalization
across different goals.
Pushing the envelope in deep visual recognition for mobile platforms
Authors: Lorenzo Alvino
Date: 2017-10-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object, Object Discovery

Image classification is the task of assigning to an input image a label from
a fixed set of categories. One of its most important applicative fields is that
of robotics, in particular the needing of a robot to be aware of what's around
and the consequent exploitation of that information as a benefit for its tasks.
In this work we consider the problem of a robot that enters a new environment
and wants to understand visual data coming from its camera, so to extract
knowledge from them. As main novelty we want to overcome the needing of a
physical robot, as it could be expensive and unhandy, so to hopefully enhance,
speed up and ease the research in this field. That's why we propose to develop
an application for a mobile platform that wraps several deep visual recognition
tasks. First we deal with a simple Image classification, testing a model
obtained from an AlexNet trained on the ILSVRC 2012 dataset. Several photo
settings are considered to better understand which factors affect most the
quality of classification. For the same purpose we are interested to integrate
the classification task with an extra module dealing with segmentation of the
object inside the image. In particular we propose a technique for extracting
the object shape and moving out all the background, so to focus the
classification only on the region occupied by the object. Another significant
task that is included is that of object discovery. Its purpose is to simulate
the situation in which the robot needs a certain object to complete one of its
activities. It starts searching for what it needs by looking around and trying
to understand the location of the object by scanning the surrounding
environment. Finally we provide a tool for dealing with the creation of
customized task-specific databases, meant to better suit to one's needing in a
particular vision task.
Dual-reference Face Retrieval
Authors: BingZhang Hu, Feng Zheng, Ling Shao
Date: 2017-06-02 00:00:00
Tasks: Retrieval

Face retrieval has received much attention over the past few decades, and
many efforts have been made in retrieving face images against pose,
illumination, and expression variations. However, the conventional works fail
to meet the requirements of a potential and novel task --- retrieving a
person's face image at a specific age, especially when the specific 'age' is
not given as a numeral, i.e. 'retrieving someone's image at the similar age
period shown by another person's image'. To tackle this problem, we propose a
dual reference face retrieval framework in this paper, where the system takes
two inputs: an identity reference image which indicates the target identity and
an age reference image which reflects the target age. In our framework, the raw
images are first projected on a joint manifold, which preserves both the age
and identity locality. Then two similarity metrics of age and identity are
exploited and optimized by utilizing our proposed quartet-based model. The
experiments show promising results, outperforming hierarchical methods.
An influence-based fast preceding questionnaire model for elderly assessments
Authors: Tong Mo, Rong Zhang, Weiping Li, Jingbo Zhang, Zhonghai Wu, Wei Tan
Date: 2017-11-22 00:00:00

To improve the efficiency of elderly assessments, an influence-based fast
preceding questionnaire model (FPQM) is proposed. Compared with traditional
assessments, the FPQM optimizes questionnaires by reordering their attributes.
The values of low-ranking attributes can be predicted by the values of the
high-ranking attributes. Therefore, the number of attributes can be reduced
without redesigning the questionnaires. A new function for calculating the
influence of the attributes is proposed based on probability theory. Reordering
and reducing algorithms are given based on the attributes' influences. The
model is verified through a practical application. The practice in an
elderly-care company shows that the FPQM can reduce the number of attributes by
90.56% with a prediction accuracy of 98.39%. Compared with other methods, such
as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best
performance. In addition, the FPQM can also be applied to other questionnaires.
On the ERM Principle with Networked Data
Authors: Yuanhong Wang, Yuyi Wang, Xingwu Liu, Juhua Pu
Date: 2017-11-12 00:00:00
Tasks: Learning-To-Rank, Link Prediction

Networked data, in which every training example involves two objects and may
share some common objects with others, is used in many machine learning tasks
such as learning to rank and link prediction. A challenge of learning from
networked examples is that target values are not known for some pairs of
objects. In this case, neither the classical i.i.d.\ assumption nor techniques
based on complete U-statistics can be used. Most existing theoretical results
of this problem only deal with the classical empirical risk minimization (ERM)
principle that always weights every example equally, but this strategy leads to
unsatisfactory bounds. We consider general weighted ERM and show new universal
risk bounds for this problem. These new bounds naturally define an optimization
problem which leads to appropriate weights for networked examples. Though this
optimization problem is not convex in general, we devise a new fully
polynomial-time approximation scheme (FPTAS) to solve it.