pwc-archive/papers-with-abstracts huggingface dataset:
A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
Authors: Mohamed Reda Bouadjenek, Esther Pacitti, Maximilien Servajean, Florent Masseglia, Amr El Abbadi
Date: 2018-07-16 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative Filtering (CF) is one of the most commonly used recommendation
methods. CF consists in predicting whether, or how much, a user will like (or
dislike) an item by leveraging the knowledge of the user's preferences as well
as that of other users. In practice, users interact and express their opinion
on only a small subset of items, which makes the corresponding user-item rating
matrix very sparse. Such data sparsity yields two main problems for recommender
systems: (1) the lack of data to effectively model users' preferences, and (2)
the lack of data to effectively model item characteristics. However, there are
often many other data sources that are available to a recommender system
provider, which can describe user interests and item characteristics (e.g.,
users' social network, tags associated to items, etc.). These valuable data
sources may supply useful information to enhance a recommendation system in
modeling users' preferences and item characteristics more accurately and thus,
hopefully, to make recommenders more precise. For various reasons, these data
sources may be managed by clusters of different data centers, thus requiring
the development of distributed solutions. In this paper, we propose a new
distributed collaborative filtering algorithm, which exploits and combines
multiple and diverse data sources to improve recommendation quality. Our
experimental evaluation using real datasets shows the effectiveness of our
algorithm compared to state-of-the-art recommendation algorithms.
Machine Learning with Membership Privacy using Adversarial Regularization
Authors: Milad Nasr, Reza Shokri, Amir Houmansadr
Date: 2018-07-16 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Inference Attack, Membership Inference Attack

Machine learning models leak information about the datasets on which they are
trained. An adversary can build an algorithm to trace the individual members of
a model's training dataset. As a fundamental inference attack, he aims to
distinguish between data points that were part of the model's training set and
any other data points from the same distribution. This is known as the tracing
(and also membership inference) attack. In this paper, we focus on such attacks
against black-box models, where the adversary can only observe the output of
the model, but not its parameters. This is the current setting of machine
learning as a service in the Internet.
  We introduce a privacy mechanism to train machine learning models that
provably achieve membership privacy: the model's predictions on its training
data are indistinguishable from its predictions on other data points from the
same distribution. We design a strategic mechanism where the privacy mechanism
anticipates the membership inference attacks. The objective is to train a model
such that not only does it have the minimum prediction error (high utility),
but also it is the most robust model against its corresponding strongest
inference attack (high privacy). We formalize this as a min-max game
optimization problem, and design an adversarial training algorithm that
minimizes the classification loss of the model as well as the maximum gain of
the membership inference attack against it. This strategy, which guarantees
membership privacy (as prediction indistinguishability), acts also as a strong
regularizer and significantly generalizes the model.
  We evaluate our privacy mechanism on deep neural networks using different
benchmark datasets. We show that our min-max strategy can mitigate the risk of
membership inference attacks (close to the random guess) with a negligible cost
in terms of the classification error.
Assessing fish abundance from underwater video using deep neural networks
Authors: Ranju Mandal, Rod M. Connolly, Thomas A. Schlacherz, Bela Stantic
Date: 2018-07-16 00:00:00
Tasks: Diversity, Fish Detection, object-detection, Object Detection, Region Proposal

Uses of underwater videos to assess diversity and abundance of fish are being
rapidly adopted by marine biologists. Manual processing of videos for
quantification by human analysts is time and labour intensive. Automatic
processing of videos can be employed to achieve the objectives in a cost and
time-efficient way. The aim is to build an accurate and reliable fish detection
and recognition system, which is important for an autonomous robotic platform.
However, there are many challenges involved in this task (e.g. complex
background, deformation, low resolution and light propagation). Recent
advancement in the deep neural network has led to the development of object
detection and recognition in real time scenarios. An end-to-end deep
learning-based architecture is introduced which outperformed the state of the
art methods and first of its kind on fish assessment task. A Region Proposal
Network (RPN) introduced by an object detector termed as Faster R-CNN was
combined with three classification networks for detection and recognition of
fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy
of 82.4% (mAP) obtained from the experiments are much higher than previously
proposed methods.
Manifold Adversarial Learning
Authors: Shufei Zhang, Kai-Zhu Huang, Jianke Zhu, Yang Liu
Date: 2018-07-16 00:00:00

Recently proposed adversarial training methods show the robustness to both adversarial and original examples and achieve state-of-the-art results in supervised and semi-supervised learning. All the existing adversarial training methods consider only how the worst perturbed examples (i.e., adversarial examples) could affect the model output. Despite their success, we argue that such setting may be in lack of generalization, since the output space (or label space) is apparently less informative.In this paper, we propose a novel method, called Manifold Adversarial Training (MAT). MAT manages to build an adversarial framework based on how the worst perturbation could affect the distributional manifold rather than the output space. Particularly, a latent data space with the Gaussian Mixture Model (GMM) will be first derived.On one hand, MAT tries to perturb the input samples in the way that would rough the distributional manifold the worst. On the other hand, the deep learning model is trained trying to promote in the latent space the manifold smoothness, measured by the variation of Gaussian mixtures (given the local perturbation around the data point). Importantly, since the latent space is more informative than the output space, the proposed MAT can learn better a robust and compact data representation, leading to further performance improvement. The proposed MAT is important in that it can be considered as a superset of one recently-proposed discriminative feature learning approach called center loss. We conducted a series of experiments in both supervised and semi-supervised learning on three benchmark data sets, showing that the proposed MAT can achieve remarkable performance, much better than those of the state-of-the-art adversarial approaches. We also present a series of visualization which could generate further understanding or explanation on adversarial examples.
Remember and Forget for Experience Replay
Authors: Guido Novati, Petros Koumoutsakos
Date: 2018-07-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Experience replay (ER) is a fundamental component of off-policy deep reinforcement learning (RL). ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency. However, the accuracy of such updates may deteriorate when the policy diverges from past behaviors and can undermine the performance of ER. Many algorithms mitigate this issue by tuning hyper-parameters to slow down policy changes. An alternative is to actively enforce the similarity between policy and the experiences in the replay memory. We introduce Remember and Forget Experience Replay (ReF-ER), a novel method that can enhance RL algorithms with parameterized policies. ReF-ER (1) skips gradients computed from experiences that are too unlikely with the current policy and (2) regulates policy changes within a trust region of the replayed behaviors. We couple ReF-ER with Q-learning, deterministic policy gradient and off-policy gradient methods. We find that ReF-ER consistently improves the performance of continuous-action, off-policy RL on fully observable benchmarks and partially observable flow control problems.
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity
Authors: Takashi Matsubara, Kenta Hama, Ryosuke Tachibana, Kuniaki Uehara
Date: 2018-07-16 00:00:00
Tasks: Anomaly Detection

Accurate and automated detection of anomalous samples in a natural image
dataset can be accomplished with a probabilistic model for end-to-end modeling
of images. Such images have heterogeneous complexity, however, and a
probabilistic model overlooks simply shaped objects with small anomalies. This
is because the probabilistic model assigns undesirably lower likelihoods to
complexly shaped objects that are nevertheless consistent with set standards.
To overcome this difficulty, we propose an unregularized score for deep
generative models (DGMs), which are generative models leveraging deep neural
networks. We found that the regularization terms of the DGMs considerably
influence the anomaly score depending on the complexity of the samples. By
removing these terms, we obtain an unregularized score, which we evaluated on a
toy dataset and real-world manufacturing datasets. Empirical results
demonstrate that the unregularized score is robust to the inherent complexity
of samples and can be used to better detect anomalies.
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification
Authors: Xinxing Su, Yingtian Zou, Yu Cheng, Shuangjie Xu, Mo Yu, Pan Zhou
Date: 2018-07-16 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

We tackle the problem of person re-identification in video setting in this
paper, which has been viewed as a crucial task in many applications. Meanwhile,
it is very challenging since the task requires learning effective
representations from video sequences with heterogeneous spatial-temporal
information. We present a novel method - Spatial-Temporal Synergic Residual
Network (STSRN) for this problem. STSRN contains a spatial residual extractor,
a temporal residual processor and a spatial-temporal smooth module. The
smoother can alleviate sample noises along the spatial-temporal dimensions thus
enable STSRN extracts more robust spatial-temporal features of consecutive
frames. Extensive experiments are conducted on several challenging datasets
including iLIDS-VID, PRID2011 and MARS. The results demonstrate that the
proposed method achieves consistently superior performance over most of
state-of-the-art methods.
The EcoLexicon English Corpus as an open corpus in Sketch Engine
Authors: Pilar Leon-Arauz, Antonio San Martin, Arianne Reimerink
Date: 2018-07-16 00:00:00

The EcoLexicon English Corpus (EEC) is a 23.1-million-word corpus of
contemporary environmental texts. It was compiled by the LexiCon research group
for the development of EcoLexicon (Faber, Leon-Arauz & Reimerink 2016; San
Martin et al. 2017), a terminological knowledge base on the environment. It is
available as an open corpus in the well-known corpus query system Sketch Engine
(Kilgarriff et al. 2014), which means that any user, even without a
subscription, can freely access and query the corpus. In this paper, the EEC is
introduced by de- scribing how it was built and compiled and how it can be
queried and exploited, based both on the functionalities provided by Sketch
Engine and on the parameters in which the texts in the EEC are classified.
MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream
Authors: Vladimir V. Arlazarov, Konstantin Bulatov, Timofey Chernov, Vladimir L. Arlazarov
Date: 2018-07-16 00:00:00
Tasks: Face Detection

A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. The main goal of this paper is to present a dataset. However, in addition and as a baseline, we present evaluation results for existing methods for face detection, text line recognition, and document data extraction, using the presented dataset. (The dataset is available for download at ftp://smartengines.com/midv-500/.)
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Authors: Rumaisah Munir, Rizwan Ahmed Khan
Date: 2018-07-16 00:00:00
Tasks: Face Recognition

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images.
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
Authors: Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
Date: 2018-07-16 00:00:00
Tasks: Gaussian Processes, Sensitivity

We introduce a novel paradigm for learning non-parametric drift and diffusion
functions for stochastic differential equation (SDE). The proposed model learns
to simulate path distributions that match observations with non-uniform time
increments and arbitrary sparseness, which is in contrast with gradient
matching that does not optimize simulated responses. We formulate sensitivity
equations for learning and demonstrate that our general stochastic distribution
optimisation leads to robust and efficient learning of SDE systems.
BRIEF: Backward Reduction of CNNs with Information Flow Analysis
Authors: Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang
Date: 2018-07-16 00:00:00

This paper proposes BRIEF, a backward reduction algorithm that explores
compact CNN-model designs from the information flow perspective. This algorithm
can remove substantial non-zero weighting parameters (redundant neural
channels) of a network by considering its dynamic behavior, which traditional
model-compaction techniques cannot achieve. With the aid of our proposed
algorithm, we achieve significant model reduction on ResNet-34 in the ImageNet
scale (32.3% reduction), which is 3X better than the previous result (10.8%).
Even for highly optimized models such as SqueezeNet and MobileNet, we can
achieve additional 10.81% and 37.56% reduction, respectively, with negligible
performance degradation.
Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks
Authors: Araz Taeihagh, Hazel Si Min Lim
Date: 2018-07-16 00:00:00
Tasks: Autonomous Vehicles

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there
are concerns about the extent of these benefits and AV risks and unintended
consequences. In this article, we first examine AVs and different categories of
the technological risks associated with them. We then explore strategies that
can be adopted to address these risks, and explore emerging responses by
governments for addressing AV risks. Our analyses reveal that, thus far,
governments have in most instances avoided stringent measures in order to
promote AV developments and the majority of responses are non-binding and focus
on creating councils or working groups to better explore AV implications. The
US has been active in introducing legislations to address issues related to
privacy and cybersecurity. The UK and Germany, in particular, have enacted laws
to address liability issues, other countries mostly acknowledge these issues,
but have yet to implement specific strategies. To address privacy and
cybersecurity risks strategies ranging from introduction or amendment of non-AV
specific legislation to creating working groups have been adopted. Much less
attention has been paid to issues such as environmental and employment risks,
although a few governments have begun programmes to retrain workers who might
be negatively affected.
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models
Authors: Xin-Yi Tong, Gui-Song Xia, Qikai Lu, Huanfeng Shen, Shengyang Li, Shucheng You, Liangpei Zhang
Date: 2018-07-16 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Land Cover Classification, Remote Sensing Image Classification, Segmentation Of Remote Sensing Imagery, Semantic Segmentation, Transfer Learning

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification.
Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest
Authors: Suhita Ray
Date: 2018-07-16 00:00:00
Tasks: Classification, General Classification, Representation Learning

In this report we propose a classification technique for skin lesion images
as a part of our submission for ISIC 2018 Challenge in Skin Lesion Analysis
Towards Melanoma Detection. Our data was extracted from the ISIC 2018: Skin
Lesion Analysis Towards Melanoma Detection grand challenge datasets. The
features are extracted through a Convolutional Neural Network, in our case
ResNet50 and then using these features we train a DeepForest, having cascading
layers, to classify our skin lesion images. We know that Convolutional Neural
Networks are a state-of-the-art technique in representation learning for
images, with the convolutional filters learning to detect features from images
through backpropagation. These features are then usually fed to a classifier
like a softmax layer or other such classifiers for classification tasks. In our
case we do not use the traditional backpropagation method and train a softmax
layer for classification. Instead, we use Deep Forest, a novel decision tree
ensemble approach with performance highly competitive to deep neural networks
in a broad range of tasks. Thus we use a ResNet50 to extract the features from
skin lesion images and then use the Deep Forest to classify these images. This
method has been used because Deep Forest has been found to be hugely efficient
in areas where there are only small-scale training data available. Also as the
Deep Forest network decides its complexity by itself, it also caters to the
problem of dataset imbalance we faced in this problem.
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs
Authors: Thanuja Dharmasiri, Andrew Spek, Tom Drummond
Date: 2018-07-16 00:00:00
Tasks: Depth Estimation, Depth Prediction, Motion Estimation, motion prediction, Optical Flow Estimation, Pose Estimation, Prediction, Simultaneous Localization and Mapping

Recovering structure and motion parameters given a image pair or a sequence
of images is a well studied problem in computer vision. This is often achieved
by employing Structure from Motion (SfM) or Simultaneous Localization and
Mapping (SLAM) algorithms based on the real-time requirements. Recently, with
the advent of Convolutional Neural Networks (CNNs) researchers have explored
the possibility of using machine learning techniques to reconstruct the 3D
structure of a scene and jointly predict the camera pose. In this work, we
present a framework that achieves state-of-the-art performance on single image
depth prediction for both indoor and outdoor scenes. The depth prediction
system is then extended to predict optical flow and ultimately the camera pose
and trained end-to-end. Our motion estimation framework outperforms the
previous motion prediction systems and we also demonstrate that the
state-of-the-art metric depths can be further improved using the knowledge of
pose.