October 18, 2019

3177 words 15 mins read

Paper Group ANR 668

SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression. Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization. Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction. Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optim …

SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression


Title	SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression
Authors	Ayesha Gurnani, Kenil Shah, Vandit Gajjar, Viraj Mavani, Yash Khandhediya
Abstract	How can we improve the facial soft-biometric classification with help of the human visual system? This paper explores the use of saliency which is equivalent to the human visual system to classify Age, Gender and Facial Expression soft-biometric for facial images. Using the Deep Multi-level Network (ML-Net) [1] and off-the-shelf face detector [2], we propose our approach - SAF-BAGE, which first detects the face in the test image, increases the Bounding Box (B-Box) margin by 30%, finds the saliency map using ML-Net, with 30% reweighted ratio of saliency map, it multiplies with the input cropped face and extracts the Convolutional Neural Networks (CNN) predictions on the multiplied reweighted salient face. Our CNN uses the model AlexNet [3], which is pre-trained on ImageNet. The proposed approach surpasses the performance of other approaches, increasing the state-of-the-art by approximately 0.8% on the widely-used Adience [28] dataset for Age and Gender classification and by nearly 3% on the recent AffectNet [36] dataset for Facial Expression classification. We hope our simple, reproducible and effective approach will help ease future research in facial soft-biometric classification using saliency.
Tasks	Age And Gender Classification
Published	2018-03-13
URL	http://arxiv.org/abs/1803.05719v2
PDF	http://arxiv.org/pdf/1803.05719v2.pdf
PWC	https://paperswithcode.com/paper/saf-bage-salient-approach-for-facial-soft
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Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization


Title	Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization
Authors	Christelle Marfaing, Alexandre Garcia
Abstract	The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. This human intervention is used to label new examples and improve the classification accuracy of the latter. Such a setting is not adapted in the case of fraud detection with financial data in European countries. Actually, as a human verification is mandatory to consider a fraud as really detected, it is not necessary to focus on improving the classifier. We introduce the setting of ‘Computer-assisted fraud detection’ where the goal is to minimize the number of non fraudulent operations submitted to an oracle. The existing methods are applied to this task and we show that a simple meta-algorithm provides competitive results in this scenario on benchmark datasets.
Tasks	Active Learning, Fraud Detection
Published	2018-11-20
URL	http://arxiv.org/abs/1811.08212v1
PDF	http://arxiv.org/pdf/1811.08212v1.pdf
PWC	https://paperswithcode.com/paper/computer-assisted-fraud-detection-from-active
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Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction


Title	Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
Authors	Bolun Zheng, Yaowu Chen, Xiang Tian, Fan Zhou, Xuesong Liu
Abstract	Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different. Moreover, these methods train a specific model for each compression quality and require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with the pixel position labeling map and the quantization tables as inputs. Specifically, we proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN. A dense block was introduced to improve the performance of extractor in DRU. The implicit dual-domain translation allows the IDCN to handle color images with the discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with little performance sacrifice and demonstrates great potential for practical applications.
Tasks	Color Image Compression Artifact Reduction, Image Compression, Quantization
Published	2018-10-18
URL	https://arxiv.org/abs/1810.08042v3
PDF	https://arxiv.org/pdf/1810.08042v3.pdf
PWC	https://paperswithcode.com/paper/implicit-dual-domain-convolutional-network
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Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization


Title	Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Authors	Zhaozhong Chen, Christoffer Heckman, Simon Julier, Nisar Ahmed
Abstract	Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models for non-white noise, etc. Conventional optimization techniques for tuning can get stuck in poor local minima and can be expensive to implement with real sensor data. To address these issues, a new “black box” Bayesian optimization strategy is developed for automatically tuning Kalman filters. In this approach, performance is characterized by one of two stochastic objective functions: normalized estimation error squared (NEES) when ground truth state models are available, or the normalized innovation error squared (NIS) when only sensor data is available. By intelligently sampling the parameter space to both learn and exploit a nonparametric Gaussian process surrogate function for the NEES/NIS costs, Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results.
Tasks	Simultaneous Localization and Mapping
Published	2018-07-23
URL	http://arxiv.org/abs/1807.08855v1
PDF	http://arxiv.org/pdf/1807.08855v1.pdf
PWC	https://paperswithcode.com/paper/weak-in-the-nees-auto-tuning-kalman-filters
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Complex Network Analysis of Men Single ATP Tennis Matches


Title	Complex Network Analysis of Men Single ATP Tennis Matches
Authors	Umberto Michieli
Abstract	Who are the most significant players in the history of men tennis? Is the official ATP ranking system fair in evaluating players scores? Which players deserved the most contemplation looking at their match records? Which players have never faced yet and are likely to play against in the future? Those are just some of the questions developed in this paper supported by data updated at April 2018. In order to give an answer to the aforementioned questions, complex network science techniques have been applied to some representations of the network of men singles tennis matches. Additionally, a new predictive algorithm is proposed in order to forecast the winner of a match.
Tasks
Published	2018-04-22
URL	http://arxiv.org/abs/1804.08138v1
PDF	http://arxiv.org/pdf/1804.08138v1.pdf
PWC	https://paperswithcode.com/paper/complex-network-analysis-of-men-single-atp
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Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach


Title	Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach
Authors	Utkarsh Porwal, Smruthi Mukund
Abstract	Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modified version of the Landsat satellite dataset, the modified version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show significant improvement over the baseline methods.
Tasks	Fraud Detection, Outlier Detection
Published	2018-11-06
URL	https://arxiv.org/abs/1811.02196v2
PDF	https://arxiv.org/pdf/1811.02196v2.pdf
PWC	https://paperswithcode.com/paper/credit-card-fraud-detection-in-e-commerce-an
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A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients


Title	A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Authors	Arnulf Jentzen, Diyora Salimova, Timo Welti
Abstract	In recent years deep artificial neural networks (DNNs) have been successfully employed in numerical simulations for a multitude of computational problems including, for example, object and face recognition, natural language processing, fraud detection, computational advertisement, and numerical approximations of partial differential equations (PDEs). These numerical simulations indicate that DNNs seem to possess the fundamental flexibility to overcome the curse of dimensionality in the sense that the number of real parameters used to describe the DNN grows at most polynomially in both the reciprocal of the prescribed approximation accuracy $ \varepsilon > 0 $ and the dimension $ d \in \mathbb{N}$ of the function which the DNN aims to approximate in such computational problems. There is also a large number of rigorous mathematical approximation results for artificial neural networks in the scientific literature but there are only a few special situations where results in the literature can rigorously justify the success of DNNs in high-dimensional function approximation. The key contribution of this paper is to reveal that DNNs do overcome the curse of dimensionality in the numerical approximation of Kolmogorov PDEs with constant diffusion and nonlinear drift coefficients. We prove that the number of parameters used to describe the employed DNN grows at most polynomially in both the PDE dimension $ d \in \mathbb{N}$ and the reciprocal of the prescribed approximation accuracy $ \varepsilon > 0 $. A crucial ingredient in our proof is the fact that the artificial neural network used to approximate the solution of the PDE is indeed a deep artificial neural network with a large number of hidden layers.
Tasks	Face Recognition, Fraud Detection
Published	2018-09-19
URL	https://arxiv.org/abs/1809.07321v2
PDF	https://arxiv.org/pdf/1809.07321v2.pdf
PWC	https://paperswithcode.com/paper/a-proof-that-deep-artificial-neural-networks
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Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System


Title	Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System
Authors	Takayuki Yamada
Abstract	A unified method for extracting geometric shape features from binary image data using a steady state partial differential equation (PDE) system as a boundary value problem is presented in this paper. The PDE and functions are formulated to extract the thickness, orientation, and skeleton simultaneously. The main advantages of the proposed method is that the orientation is defined without derivatives and thickness computation is not imposed a topological constraint on the target shape. A one-dimensional analytical solution is provided to validate the proposed method. In addition, two-dimensional numerical examples are presented to confirm the usefulness of the proposed method.
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Published	2018-06-13
URL	http://arxiv.org/abs/1806.05299v3
PDF	http://arxiv.org/pdf/1806.05299v3.pdf
PWC	https://paperswithcode.com/paper/geometric-shape-features-extraction-using-a
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Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data


Title	Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data
Authors	Kathryn Gray, Daniel Smolyak, Sarkhan Badirli, George Mohler
Abstract	Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.
Tasks	Anomaly Detection, Feature Engineering, Fraud Detection
Published	2018-09-08
URL	http://arxiv.org/abs/1809.02728v1
PDF	http://arxiv.org/pdf/1809.02728v1.pdf
PWC	https://paperswithcode.com/paper/coupled-igmm-gans-for-deep-multimodal-anomaly
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Multi-Dimensional Scaling on Groups


Title	Multi-Dimensional Scaling on Groups
Authors	Mark Blumstein, Henry Kvinge
Abstract	Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental “frequencies” (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.
Tasks	Dimensionality Reduction
Published	2018-12-08
URL	https://arxiv.org/abs/1812.03362v2
PDF	https://arxiv.org/pdf/1812.03362v2.pdf
PWC	https://paperswithcode.com/paper/letting-symmetry-guide-visualization
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Weakly Supervised Grammatical Error Correction using Iterative Decoding


Title	Weakly Supervised Grammatical Error Correction using Iterative Decoding
Authors	Jared Lichtarge, Christopher Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar
Abstract	We describe an approach to Grammatical Error Correction (GEC) that is effective at making use of models trained on large amounts of weakly supervised bitext. We train the Transformer sequence-to-sequence model on 4B tokens of Wikipedia revisions and employ an iterative decoding strategy that is tailored to the loosely-supervised nature of the Wikipedia training corpus. Finetuning on the Lang-8 corpus and ensembling yields an F0.5 of 58.3 on the CoNLL’14 benchmark and a GLEU of 62.4 on JFLEG. The combination of weakly supervised training and iterative decoding obtains an F0.5 of 48.2 on CoNLL’14 even without using any labeled GEC data.
Tasks	Grammatical Error Correction
Published	2018-10-31
URL	http://arxiv.org/abs/1811.01710v1
PDF	http://arxiv.org/pdf/1811.01710v1.pdf
PWC	https://paperswithcode.com/paper/weakly-supervised-grammatical-error
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Simple and Effective Semi-Supervised Question Answering


Title	Simple and Effective Semi-Supervised Question Answering
Authors	Bhuwan Dhingra, Danish Pruthi, Dheeraj Rajagopal
Abstract	Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. However, large domain specific annotated corpora are limited and expensive to construct. In this work, we envision a system where the end user specifies a set of base documents and only a few labelled examples. Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples. We evaluate our proposed system across three diverse datasets from different domains, and find it to be highly effective with very little labeled data. We attain more than 50% F1 score on SQuAD and TriviaQA with less than a thousand labelled examples. We are also releasing a set of 3.2M cloze-style questions for practitioners to use while building QA systems.
Tasks	Question Answering
Published	2018-04-02
URL	http://arxiv.org/abs/1804.00720v1
PDF	http://arxiv.org/pdf/1804.00720v1.pdf
PWC	https://paperswithcode.com/paper/simple-and-effective-semi-supervised-question
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VOS: a Method for Variational Oversampling of Imbalanced Data


Title	VOS: a Method for Variational Oversampling of Imbalanced Data
Authors	Val Andrei Fajardo, David Findlay, Roshanak Houmanfar, Charu Jaiswal, Jiaxi Liang, Honglei Xie
Abstract	Class imbalanced datasets are common in real-world applications that range from credit card fraud detection to rare disease diagnostics. Several popular classification algorithms assume that classes are approximately balanced, and hence build the accompanying objective function to maximize an overall accuracy rate. In these situations, optimizing the overall accuracy will lead to highly skewed predictions towards the majority class. Moreover, the negative business impact resulting from false positives (positive samples incorrectly classified as negative) can be detrimental. Many methods have been proposed to address the class imbalance problem, including methods such as over-sampling, under-sampling and cost-sensitive methods. In this paper, we consider the over-sampling method, where the aim is to augment the original dataset with synthetically created observations of the minority classes. In particular, inspired by the recent advances in generative modelling techniques (e.g., Variational Inference and Generative Adversarial Networks), we introduce a new oversampling technique based on variational autoencoders. Our experiments show that the new method is superior in augmenting datasets for downstream classification tasks when compared to traditional oversampling methods.
Tasks	Fraud Detection
Published	2018-09-07
URL	http://arxiv.org/abs/1809.02596v1
PDF	http://arxiv.org/pdf/1809.02596v1.pdf
PWC	https://paperswithcode.com/paper/vos-a-method-for-variational-oversampling-of
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A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management


Title	A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management
Authors	Leander Löw, Martin Spindler, Eike Brechmann
Abstract	Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem. Health care claims often have a data structure that is hierarchical and of variable length. We propose one model based on piecewise feed forward neural networks (deep learning) and another model based on self-attention neural networks for the task of claim management. We show that the proposed methods outperform bag-of-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims. The proposed self-attention method performs the best.
Tasks	Fraud Detection
Published	2018-08-30
URL	http://arxiv.org/abs/1808.10543v1
PDF	http://arxiv.org/pdf/1808.10543v1.pdf
PWC	https://paperswithcode.com/paper/a-self-attention-network-for-hierarchical
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ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks


Title	ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks
Authors	Zied Elloumi, Laurent Besacier, Olivier Galibert, Juliette Kahn, Benjamin Lecouteux
Abstract	In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably predicts the WER distribution on a collection of speech recordings.
Tasks
Published	2018-04-23
URL	http://arxiv.org/abs/1804.08477v1
PDF	http://arxiv.org/pdf/1804.08477v1.pdf
PWC	https://paperswithcode.com/paper/asr-performance-prediction-on-unseen
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