pwc-archive/papers-with-abstracts huggingface dataset:
Can Computers Create Art?
Authors: Aaron Hertzmann
Date: 2018-01-13 00:00:00

This essay discusses whether computers, using Artificial Intelligence (AI),
could create art. First, the history of technologies that automated aspects of
art is surveyed, including photography and animation. In each case, there were
initial fears and denial of the technology, followed by a blossoming of new
creative and professional opportunities for artists. The current hype and
reality of Artificial Intelligence (AI) tools for art making is then discussed,
together with predictions about how AI tools will be used. It is then
speculated about whether it could ever happen that AI systems could be credited
with authorship of artwork. It is theorized that art is something created by
social agents, and so computers cannot be credited with authorship of art in
our current understanding. A few ways that this could change are also
hypothesized.
Improving Knowledge Graph Embedding Using Simple Constraints
Authors: Boyang Ding, Quan Wang, Bin Wang, Li Guo
Date: 2018-05-07 00:00:00
Proceeding: ACL 2018 7
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
current research. Early works performed this task via simple models developed
over KG triples. Recent attempts focused on either designing more complicated
triple scoring models, or incorporating extra information beyond triples. This
paper, by contrast, investigates the potential of using very simple constraints
to improve KG embedding. We examine non-negativity constraints on entity
representations and approximate entailment constraints on relation
representations. The former help to learn compact and interpretable
representations for entities. The latter further encode regularities of logical
entailment between relations into their distributed representations. These
constraints impose prior beliefs upon the structure of the embedding space,
without negative impacts on efficiency or scalability. Evaluation on WordNet,
Freebase, and DBpedia shows that our approach is simple yet surprisingly
effective, significantly and consistently outperforming competitive baselines.
The constraints imposed indeed improve model interpretability, leading to a
substantially increased structuring of the embedding space. Code and data are
available at https://github.com/iieir-km/ComplEx-NNE_AER.
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection
Authors: Qiangpeng Yang, Mengli Cheng, Wenmeng Zhou, Yan Chen, Minghui Qiu, Wei. Lin, Wei Chu
Date: 2018-05-03 00:00:00
Tasks: Multi-Oriented Scene Text Detection, object-detection, Object Detection, Optical Character Recognition (OCR), Scene Text Detection, Text Detection

Incidental scene text detection, especially for multi-oriented text regions,
is one of the most challenging tasks in many computer vision applications.
Different from the common object detection task, scene text often suffers from
a large variance of aspect ratio, scale, and orientation. To solve this
problem, we propose a novel end-to-end scene text detector IncepText from an
instance-aware segmentation perspective. We design a novel Inception-Text
module and introduce deformable PSROI pooling to deal with multi-oriented text
detection. Extensive experiments on ICDAR2015, RCTW-17, and MSRA-TD500 datasets
demonstrate our method's superiority in terms of both effectiveness and
efficiency. Our proposed method achieves 1st place result on ICDAR2015
challenge and the state-of-the-art performance on other datasets. Moreover, we
have released our implementation as an OCR product which is available for
public access.
Revisiting Temporal Modeling for Video-based Person ReID
Authors: Jiyang Gao, Ram Nevatia
Date: 2018-05-05 00:00:00

Video-based person reID is an important task, which has received much
attention in recent years due to the increasing demand in surveillance and
camera networks. A typical video-based person reID system consists of three
parts: an image-level feature extractor (e.g. CNN), a temporal modeling method
to aggregate temporal features and a loss function. Although many methods on
temporal modeling have been proposed, it is hard to directly compare these
methods, because the choice of feature extractor and loss function also have a
large impact on the final performance. We comprehensively study and compare
four different temporal modeling methods (temporal pooling, temporal attention,
RNN and 3D convnets) for video-based person reID. We also propose a new
attention generation network which adopts temporal convolution to extract
temporal information among frames. The evaluation is done on the MARS dataset,
and our methods outperform state-of-the-art methods by a large margin. Our
source codes are released at https://github.com/jiyanggao/Video-Person-ReID.
A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing
Authors: Chaitanya Nagpal, Shiv Ram Dubey
Date: 2018-05-08 00:00:00
Tasks: Face Anti-Spoofing, image-classification, Image Classification

In the current era, biometric based access control is becoming more popular
due to its simplicity and ease to use by the users. It reduces the manual work
of identity recognition and facilitates the automatic processing. The face is
one of the most important biometric visual information that can be easily
captured without user cooperation in an uncontrolled environment. Precise
detection of spoofed faces should be on the high priority to make face based
identity recognition and access control robust against possible attacks. The
recently evolved Convolutional Neural Network (CNN) based deep learning
technique has proven as one of the excellent method to deal with the visual
information very effectively. The CNN learns the hierarchical features at
intermediate layers automatically from the data. Several CNN based methods such
as Inception and ResNet have shown outstanding performance for image
classification problem. This paper does a performance evaluation of CNNs for
face anti-spoofing. The Inception and ResNet CNN architectures are used in this
study. The results are computed over benchmark MSU Mobile Face Spoofing
Database. The experiments are done by considering the different aspects such as
the depth of the model, random weight initialization vs weight transfer, fine
tuning vs training from scratch and different learning rate. The favorable
results are obtained using these CNN architectures for face anti-spoofing in
different settings.
FFNet: Video Fast-Forwarding via Reinforcement Learning
Authors: Shuyue Lan, Rameswar Panda, Qi Zhu, Amit K. Roy-Chowdhury
Date: 2018-05-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Video Summarization

For many applications with limited computation, communication, storage and
energy resources, there is an imperative need of computer vision methods that
could select an informative subset of the input video for efficient processing
at or near real time. In the literature, there are two relevant groups of
approaches: generating a trailer for a video or fast-forwarding while
watching/processing the video. The first group is supported by video
summarization techniques, which require processing of the entire video to
select an important subset for showing to users. In the second group, current
fast-forwarding methods depend on either manual control or automatic adaptation
of playback speed, which often do not present an accurate representation and
may still require processing of every frame. In this paper, we introduce
FastForwardNet (FFNet), a reinforcement learning agent that gets inspiration
from video summarization and does fast-forwarding differently. It is an online
framework that automatically fast-forwards a video and presents a
representative subset of frames to users on the fly. It does not require
processing the entire video, but just the portion that is selected by the
fast-forward agent, which makes the process very computationally efficient. The
online nature of our proposed method also enables the users to begin
fast-forwarding at any point of the video. Experiments on two real-world
datasets demonstrate that our method can provide better representation of the
input video with much less processing requirement.
Deep Texture and Structure Aware Filtering Network for Image Smoothing
Authors: Kaiyue Lu, ShaoDi You, Nick Barnes
Date: 2017-12-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: image smoothing

Image smoothing is a fundamental task in computer vision, that aims to retain
salient structures and remove insignificant textures. In this paper, we aim to
address the fundamental shortcomings of existing image smoothing methods, which
cannot properly distinguish textures and structures with similar low-level
appearance. While deep learning approaches have started to explore the
preservation of structure through image smoothing, existing work does not yet
properly address textures. To this end, we generate a large dataset by blending
natural textures with clean structure-only images, and then build a texture
prediction network (TPN) that predicts the location and magnitude of textures.
We then combine the TPN with a semantic structure prediction network (SPN) so
that the final texture and structure aware filtering network (TSAFN) is able to
identify the textures to remove ("texture-awareness") and the structures to
preserve ("structure-awareness"). The proposed model is easy to understand and
implement, and shows excellent performance on real images in the wild as well
as our generated dataset.
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs
Authors: Aparna Balagopalan, Satya Gorti, Mathieu Ravaut, Raeid Saqur
Date: 2018-05-08 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) have seen steep ascension to the peak
of ML research zeitgeist in recent years. Mostly catalyzed by its success in
the domain of image generation, the technique has seen wide range of adoption
in a variety of other problem domains. Although GANs have had a lot of success
in producing more realistic images than other approaches, they have only seen
limited use for text sequences. Generation of longer sequences compounds this
problem. Most recently, SeqGAN (Yu et al., 2017) has shown improvements in
adversarial evaluation and results with human evaluation compared to a MLE
based trained baseline. The main contributions of this paper are three-fold: 1.
We show results for sequence generation using a GAN architecture with efficient
policy gradient estimators, 2. We attain improved training stability, and 3. We
perform a comparative study of recent unbiased low variance gradient estimation
techniques such as REBAR (Tucker et al., 2017), RELAX (Grathwohl et al., 2018)
and REINFORCE (Williams, 1992). Using a simple grammar on synthetic datasets
with varying length, we indicate the quality of sequences generated by the
model.
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
Authors: Sarah Jane Hamilton, Andreas Hauptmann
Date: 2017-11-08 00:00:00
Tasks: Image Generation

The mathematical problem for Electrical Impedance Tomography (EIT) is a
highly nonlinear ill-posed inverse problem requiring carefully designed
reconstruction procedures to ensure reliable image generation. D-bar methods
are based on a rigorous mathematical analysis and provide robust direct
reconstructions by using a low-pass filtering of the associated nonlinear
Fourier data. Similarly to low-pass filtering of linear Fourier data, only
using low frequencies in the image recovery process results in blurred images
lacking sharp features such as clear organ boundaries. Convolutional Neural
Networks provide a powerful framework for post-processing such convolved direct
reconstructions. In this study, we demonstrate that these CNN techniques lead
to sharp and reliable reconstructions even for the highly nonlinear inverse
problem of EIT. The network is trained on data sets of simulated examples and
then applied to experimental data without the need to perform an additional
transfer training. Results for absolute EIT images are presented using
experimental EIT data from the ACT4 and KIT4 EIT systems.
What game are we playing? End-to-end learning in normal and extensive form games
Authors: Chun Kai Ling, Fei Fang, J. Zico Kolter
Date: 2018-05-07 00:00:00
Tasks: Form

Although recent work in AI has made great progress in solving large,
zero-sum, extensive-form games, the underlying assumption in most past work is
that the parameters of the game itself are known to the agents. This paper
deals with the relatively under-explored but equally important "inverse"
setting, where the parameters of the underlying game are not known to all
agents, but must be learned through observations. We propose a differentiable,
end-to-end learning framework for addressing this task. In particular, we
consider a regularized version of the game, equivalent to a particular form of
quantal response equilibrium, and develop 1) a primal-dual Newton method for
finding such equilibrium points in both normal and extensive form games; and 2)
a backpropagation method that lets us analytically compute gradients of all
relevant game parameters through the solution itself. This ultimately lets us
learn the game by training in an end-to-end fashion, effectively by integrating
a "differentiable game solver" into the loop of larger deep network
architectures. We demonstrate the effectiveness of the learning method in
several settings including poker and security game tasks.
An Expectation Maximization Framework for Yule-Simon Preferential Attachment Models
Authors: Lucas Roberts, Denisa Roberts
Date: 2017-10-23 00:00:00

In this paper we develop an Expectation Maximization(EM) algorithm to estimate the parameter of a Yule-Simon distribution. The Yule-Simon distribution exhibits the "rich get richer" effect whereby an 80-20 type of rule tends to dominate. These distributions are ubiquitous in industrial settings. The EM algorithm presented provides both frequentist and Bayesian estimates of the $\lambda$ parameter. By placing the estimation method within the EM framework we are able to derive Standard errors of the resulting estimate. Additionally, we prove convergence of the Yule-Simon EM algorithm and study the rate of convergence. An explicit, closed form solution for the rate of convergence of the algorithm is given. Applications including graph node degree distribution estimation are listed.
End-to-End Refinement Guided by Pre-trained Prototypical Classifier
Authors: Junwen Bai, Zihang Lai, Runzhe Yang, Yexiang Xue, John Gregoire, Carla Gomes
Date: 2018-05-07 00:00:00

Many real-world tasks involve identifying patterns from data satisfying
background or prior knowledge. In domains like materials discovery, due to the
flaws and biases in raw experimental data, the identification of X-ray
diffraction patterns (XRD) often requires a huge amount of manual work in
finding refined phases that are similar to the ideal theoretical ones.
Automatically refining the raw XRDs utilizing the simulated theoretical data is
thus desirable. We propose imitation refinement, a novel approach to refine
imperfect input patterns, guided by a pre-trained classifier incorporating
prior knowledge from simulated theoretical data, such that the refined patterns
imitate the ideal data. The classifier is trained on the ideal simulated data
to classify patterns and learns an embedding space where each class is
represented by a prototype. The refiner learns to refine the imperfect patterns
with small modifications, such that their embeddings are closer to the
corresponding prototypes. We show that the refiner can be trained in both
supervised and unsupervised fashions. We further illustrate the effectiveness
of the proposed approach both qualitatively and quantitatively in a digit
refinement task and an X-ray diffraction pattern refinement task in materials
discovery.
Weakly Learning to Match Experts in Online Community
Authors: Yujie Qian, Jie Tang, Kan Wu
Date: 2016-11-14 00:00:00

In online question-and-answer (QA) websites like Quora, one central issue is
to find (invite) users who are able to provide answers to a given question and
at the same time would be unlikely to say "no" to the invitation. The challenge
is how to trade off the matching degree between users' expertise and the
question topic, and the likelihood of positive response from the invited users.
In this paper, we formally formulate the problem and develop a weakly
supervised factor graph (WeakFG) model to address the problem. The model
explicitly captures expertise matching degree between questions and users. To
model the likelihood that an invited user is willing to answer a specific
question, we incorporate a set of correlations based on social identity theory
into the WeakFG model. We use two different genres of datasets: QA-Expert and
Paper-Reviewer, to validate the proposed model. Our experimental results show
that the proposed model can significantly outperform (+1.5-10.7% by MAP) the
state-of-the-art algorithms for matching users (experts) with community
questions. We have also developed an online system to further demonstrate the
advantages of the proposed method.
Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment
Authors: Thomio Watanabe, Denis Wolf
Date: 2018-05-07 00:00:00
Tasks: Autonomous Driving, Decision Making

The autonomous car technology promises to replace human drivers with safer
driving systems. But although autonomous cars can become safer than human
drivers this is a long process that is going to be refined over time. Before
these vehicles are deployed on urban roads a minimum safety level must be
assured. Since the autonomous car technology is still under development there
is no standard methodology to evaluate such systems. It is important to
completely understand the technology that is being developed to design
efficient means to evaluate it. In this paper we assume safety-critical systems
reliability as a safety measure. We model an autonomous road vehicle as an
intelligent agent and we approach its evaluation from an artificial
intelligence perspective. Our focus is the evaluation of perception and
decision making systems and also to propose a systematic method to evaluate
their integration in the vehicle. We identify critical aspects of the data
dependency from the artificial intelligence state of the art models and we also
propose procedures to reproduce them.
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks
Authors: Supreeth P. Shashikumar, Amit J. Shah, Gari. D. Clifford, Shamim Nemati
Date: 2018-05-07 00:00:00
Tasks: Atrial Fibrillation Detection, Electrocardiography (ECG)

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is
difficult since many cases of AF are usually clinically silent and undiagnosed.
In particular paroxysmal AF is a form of AF that occurs occasionally, and has a
higher probability of being undetected. In this work, we present an attention
based deep learning framework for detection of paroxysmal AF episodes from a
sequence of windows. Time-frequency representation of 30 seconds recording
windows, over a 10 minute data segment, are fed sequentially into a deep
convolutional neural network for image-based feature extraction, which are then
presented to a bidirectional recurrent neural network with an attention layer
for AF detection. To demonstrate the effectiveness of the proposed framework
for transient AF detection, we use a database of 24 hour Holter
Electrocardiogram (ECG) recordings acquired from 2850 patients at the
University of Virginia heart station. The algorithm achieves an AUC of 0.94 on
the testing set, which exceeds the performance of baseline models. We also
demonstrate the cross-domain generalizablity of the approach by adapting the
learned model parameters from one recording modality (ECG) to another
(photoplethysmogram) with improved AF detection performance. The proposed high
accuracy, low false alarm algorithm for detecting paroxysmal AF has potential
applications in long-term monitoring using wearable sensors.
Learning Optical Flow via Dilated Networks and Occlusion Reasoning
Authors: Yi Zhu, Shawn Newsam
Date: 2018-05-07 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Despite the significant progress that has been made on estimating optical
flow recently, most estimation methods, including classical and deep learning
approaches, still have difficulty with multi-scale estimation, real-time
computation, and/or occlusion reasoning. In this paper, we introduce dilated
convolution and occlusion reasoning into unsupervised optical flow estimation
to address these issues. The dilated convolution allows our network to avoid
upsampling via deconvolution and the resulting gridding artifacts. Dilated
convolution also results in a smaller memory footprint which speeds up
interference. The occlusion reasoning prevents our network from learning
incorrect deformations due to occluded image regions during training. Our
proposed method outperforms state-of-the-art unsupervised approaches on the
KITTI benchmark. We also demonstrate its generalization capability by applying
it to action recognition in video.
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Authors: Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood, Mehdi Moradi
Date: 2018-05-07 00:00:00
Tasks: Anatomy, Classification, General Classification, Medical Image Analysis, Segmentation

Although deep learning can provide promising results in medical image
analysis, the lack of very large annotated datasets confines its full
potential. Furthermore, limited positive samples also create unbalanced
datasets which limit the true positive rates of trained models. As unbalanced
datasets are mostly unavoidable, it is greatly beneficial if we can extract
useful knowledge from negative samples to improve classification accuracy on
limited positive samples. To this end, we propose a new strategy for building
medical image analysis pipelines that target disease detection. We train a
discriminative segmentation model only on normal images to provide a source of
knowledge to be transferred to a disease detection classifier. We show that
using the feature maps of a trained segmentation network, deviations from
normal anatomy can be learned by a two-class classification network on an
extremely unbalanced training dataset with as little as one positive for 17
negative samples. We demonstrate that even though the segmentation network is
only trained on normal cardiac computed tomography images, the resulting
feature maps can be used to detect pericardial effusion and cardiac septal
defects with two-class convolutional classification networks.