January 26, 2020

3438 words 17 mins read

Paper Group ANR 1579

Lane Detection For Prototype Autonomous Vehicle. Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition. Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems. Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analy …

Lane Detection For Prototype Autonomous Vehicle


Title	Lane Detection For Prototype Autonomous Vehicle
Authors	Sertap Kamçı, Dogukan Aksu, Muhammed Ali Aydin
Abstract	Unmanned vehicle technologies are an area of great interest in theory and practice today. These technologies have advanced considerably after the first applications have been implemented and cause a rapid change in human life. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. By using image processing and artificial intelligence techniques, an autonomous vehicle can move successfully without a driver help. They can go from the initial point to the specified target by applying pre-defined rules. There are also rules for proper tracking of the lanes. Many accidents are caused due to insufficient follow-up of the lanes and non-compliance with these rules. The majority of these accidents also result in injury and death. In this paper, we present an autonomous vehicle prototype that follows lanes via image processing techniques, which are a major part of autonomous vehicle technology. Autonomous movement capability is provided by using some image processing algorithms such as canny edge detection, Sobel filter, etc. We implemented and tested these algorithms on the vehicle. The vehicle detected and followed the determined lanes. By that way, it went to the destination successfully.
Tasks	Autonomous Vehicles, Edge Detection, Lane Detection
Published	2019-12-11
URL	https://arxiv.org/abs/1912.05220v1
PDF	https://arxiv.org/pdf/1912.05220v1.pdf
PWC	https://paperswithcode.com/paper/lane-detection-for-prototype-autonomous
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Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition


Title	Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition
Authors	Haoqian Wang, Zhiwei Xu, Jun Xu, Wangpeng An, Lei Zhang, Qionghai Dai
Abstract	Image recognition is an important topic in computer vision and image processing, and has been mainly addressed by supervised deep learning methods, which need a large set of labeled images to achieve promising performance. However, in most cases, labeled data are expensive or even impossible to obtain, while unlabeled data are readily available from numerous free on-line resources and have been exploited to improve the performance of deep neural networks. To better exploit the power of unlabeled data for image recognition, in this paper, we propose a semi-supervised and generative approach, namely the semi-supervised self-growing generative adversarial network (SGGAN). Label inference is a key step for the success of semi-supervised learning approaches. There are two main problems in label inference: how to measure the confidence of the unlabeled data and how to generalize the classifier. We address these two problems via the generative framework and a novel convolution-block-transformation technique, respectively. To stabilize and speed up the training process of SGGAN, we employ the metric Maximum Mean Discrepancy as the feature matching objective function and achieve larger gain than the standard semi-supervised GANs (SSGANs), narrowing the gap to the supervised methods. Experiments on several benchmark datasets show the effectiveness of the proposed SGGAN on image recognition and facial attribute recognition tasks. By using the training data with only 4% labeled facial attributes, the SGGAN approach can achieve comparable accuracy with leading supervised deep learning methods with all labeled facial attributes.
Tasks
Published	2019-08-11
URL	https://arxiv.org/abs/1908.03850v1
PDF	https://arxiv.org/pdf/1908.03850v1.pdf
PWC	https://paperswithcode.com/paper/semi-supervised-self-growing-generative
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Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems


Title	Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
Authors	Christine Bauer, Markus Schedl
Abstract	Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music. The contribution of this article is three-fold. We provide several quantitative measures describing the proximity of a user’s music preference to the music mainstream. We define the measures at two levels: relating a listener’s music preferences to the global music preferences of all users, or relating them to music preferences of the user’s country. Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. We analyze differences between countries in terms of their level of mainstreaminess, uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), and investigate differences between countries in terms of listening preferences related to popular music artists. We use the standardized LFM-1b dataset, from which we analyze about 8 million listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. We conduct rating prediction experiments in which we tailor recommendations to a user’s level of preference for the music mainstream using the proposed 6 mainstreaminess measures. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.
Tasks	Recommendation Systems
Published	2019-12-14
URL	https://arxiv.org/abs/1912.06933v1
PDF	https://arxiv.org/pdf/1912.06933v1.pdf
PWC	https://paperswithcode.com/paper/global-and-country-specific-mainstreaminess
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Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis


Title	Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis
Authors	Rafael Rodrigues, Antonio M. G. Pinheiro
Abstract	Segmentation of skeletal muscles in Magnetic Resonance Images (MRI) is essential for the study of muscle physiology and diagnosis of muscular pathologies. However, manual segmentation of large MRI volumes is a time-consuming task. The state-of-the-art on algorithms for muscle segmentation in MRI is still not very extensive and is somewhat database-dependent. In this paper, an automated segmentation method based on AdaBoost classification of local texture features is presented. The texture descriptor consists of the Histogram of Oriented Gradients (HOG), Wavelet-based features, and a set of statistical measures computed from both the original and the Laplacian of Gaussian filtering of the grayscale MRI. The classifier performance suggests that texture analysis may be a helpful tool for designing a generalized and automated MRI muscle segmentation framework. Furthermore, an atlas-based approach to individual muscle segmentation is also described in this paper. The atlas is obtained by overlaying the muscle segmentation ground truth, provided by a radiologist, after image alignment using an appropriate affine transformation. Then, it is used to define the muscle labels upon the AdaBoost binary segmentation. The developed atlas method provides reasonable results when an accurate muscle tissue segmentation was obtained.
Tasks	Texture Classification
Published	2019-04-09
URL	http://arxiv.org/abs/1904.04747v1
PDF	http://arxiv.org/pdf/1904.04747v1.pdf
PWC	https://paperswithcode.com/paper/segmentation-of-skeletal-muscle-in-thigh
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Improving VAEs’ Robustness to Adversarial Attack


Title	Improving VAEs’ Robustness to Adversarial Attack
Authors	Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen Roberts, Chris Holmes
Abstract	Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image. However, how to defend against such attacks remains an open problem. We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs. Namely, we first demonstrate that methods used to obtain disentangled latent representations produce VAEs that are more robust to these attacks. However, this robustness comes at the cost of reducing the quality of the reconstructions. We, therefore, further introduce a new hierarchical VAE, the \textit{Seatbelt-VAE}, which can produce high-fidelity autoencoders that are also adversarially robust. We confirm the empirical capabilities of the Seatbelt-VAE on several different datasets and with current state-of-the-art VAE adversarial attack schemes.
Tasks	Adversarial Attack
Published	2019-06-01
URL	https://arxiv.org/abs/1906.00230v3
PDF	https://arxiv.org/pdf/1906.00230v3.pdf
PWC	https://paperswithcode.com/paper/190600230
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Local Deep-Feature Alignment for Unsupervised Dimension Reduction


Title	Local Deep-Feature Alignment for Unsupervised Dimension Reduction
Authors	Jian Zhang, Jun Yu, Dacheng Tao
Abstract	This paper presents an unsupervised deep-learning framework named Local Deep-Feature Alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local Stacked Contractive Auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.
Tasks	Dimensionality Reduction
Published	2019-04-22
URL	http://arxiv.org/abs/1904.09747v1
PDF	http://arxiv.org/pdf/1904.09747v1.pdf
PWC	https://paperswithcode.com/paper/local-deep-feature-alignment-for-unsupervised
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On Inferring Training Data Attributes in Machine Learning Models


Title	On Inferring Training Data Attributes in Machine Learning Models
Authors	Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Raghav Bhaskar, Mohamed Ali Kaafar
Abstract	A number of recent works have demonstrated that API access to machine learning models leaks information about the dataset records used to train the models. Further, the work of \cite{somesh-overfit} shows that such membership inference attacks (MIAs) may be sufficient to construct a stronger breed of attribute inference attacks (AIAs), which given a partial view of a record can guess the missing attributes. In this work, we show (to the contrary) that MIA may not be sufficient to build a successful AIA. This is because the latter requires the ability to distinguish between similar records (differing only in a few attributes), and, as we demonstrate, the current breed of MIA are unsuccessful in distinguishing member records from similar non-member records. We thus propose a relaxed notion of AIA, whose goal is to only approximately guess the missing attributes and argue that such an attack is more likely to be successful, if MIA is to be used as a subroutine for inferring training record attributes.
Tasks
Published	2019-08-28
URL	https://arxiv.org/abs/1908.10558v2
PDF	https://arxiv.org/pdf/1908.10558v2.pdf
PWC	https://paperswithcode.com/paper/on-inferring-training-data-attributes-in
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Deep Sequential Mosaicking of Fetoscopic Videos


Title	Deep Sequential Mosaicking of Fetoscopic Videos
Authors	Sophia Bano, Francisco Vasconcelos, Marcel Tella Amo, George Dwyer, Caspar Gruijthuijsen, Jan Deprest, Sebastien Ourselin, Emmanuel Vander Poorten, Tom Vercauteren, Danail Stoyanov
Abstract	Twin-to-twin transfusion syndrome treatment requires fetoscopic laser photocoagulation of placental vascular anastomoses to regulate blood flow to both fetuses. Limited field-of-view (FoV) and low visual quality during fetoscopy make it challenging to identify all vascular connections. Mosaicking can align multiple overlapping images to generate an image with increased FoV, however, existing techniques apply poorly to fetoscopy due to the low visual quality, texture paucity, and hence fail in longer sequences due to the drift accumulated over time. Deep learning techniques can facilitate in overcoming these challenges. Therefore, we present a new generalized Deep Sequential Mosaicking (DSM) framework for fetoscopic videos captured from different settings such as simulation, phantom, and real environments. DSM extends an existing deep image-based homography model to sequential data by proposing controlled data augmentation and outlier rejection methods. Unlike existing methods, DSM can handle visual variations due to specular highlights and reflection across adjacent frames, hence reducing the accumulated drift. We perform experimental validation and comparison using 5 diverse fetoscopic videos to demonstrate the robustness of our framework.
Tasks	Data Augmentation
Published	2019-07-15
URL	https://arxiv.org/abs/1907.06543v1
PDF	https://arxiv.org/pdf/1907.06543v1.pdf
PWC	https://paperswithcode.com/paper/deep-sequential-mosaicking-of-fetoscopic
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Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study


Title	Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study
Authors	Jukka Hirvasniemi, Willem Paul Gielis, Saeed Arbabi, Rintje Agricola, Willem Evert van Spil, Vahid Arbabi, Harrie Weinans
Abstract	Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fractal signature analysis in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (Kellgren-Lawrence grade (KL) > 1 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade > 0 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade > 0 at 10-year follow-up. AUCs for the models including age, gender, and body mass index to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture parameters in the models improved the prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.53). Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.
Tasks	Texture Classification
Published	2019-02-13
URL	http://arxiv.org/abs/1902.04880v1
PDF	http://arxiv.org/pdf/1902.04880v1.pdf
PWC	https://paperswithcode.com/paper/bone-texture-analysis-for-prediction-of
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Bayesian optimisation under uncertain inputs


Title	Bayesian optimisation under uncertain inputs
Authors	Rafael Oliveira, Lionel Ott, Fabio Ramos
Abstract	Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In these cases, the estimation of the actual query location is also subject to uncertainty. In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain. The algorithm employs a Gaussian process model that takes probability distributions as inputs. Theoretical results are provided for both the proposed algorithm and a conventional UCB approach within the uncertain-inputs setting. Finally, we evaluate each method’s performance experimentally, comparing them to other input noise aware BO approaches on simulated scenarios involving synthetic and real data.
Tasks	Bayesian Optimisation
Published	2019-02-21
URL	http://arxiv.org/abs/1902.07908v1
PDF	http://arxiv.org/pdf/1902.07908v1.pdf
PWC	https://paperswithcode.com/paper/bayesian-optimisation-under-uncertain-inputs
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A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training


Title	A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
Authors	Sumedh Yadav, Mathis Bode
Abstract	A scalable graphical method is presented for selecting, and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is proceeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method constitutes of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristic available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for the partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.
Tasks
Published	2019-07-24
URL	https://arxiv.org/abs/1907.10421v1
PDF	https://arxiv.org/pdf/1907.10421v1.pdf
PWC	https://paperswithcode.com/paper/a-graphical-heuristic-for-reduction-and
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Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks


Title	Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks
Authors	Yue-he Zhu, Ya-zhong Luo
Abstract	The design of low-thrust-based multitarget interplanetary missions requires a method to quickly and accurately evaluate the low-thrust transfer between any two visiting targets. Complete evaluation of the low-thrust transfer includes not only the estimation of the optimal fuel consumption but also the judgment of transfer feasibility. In this paper, a deep neural network (DNN)-based method is proposed for quickly evaluating low-thrust transfer. An efficient database generation method is developed for obtaining both the infeasible and optimal transfers. A classification DNN and a regression DNN are trained based on the infeasible and optimal transfers to judge the transfer feasibility and estimate the optimal fuel consumption, respectively. The simulation results show that the well-trained DNNs are capable of quickly determining the transfer feasibility with a correct rate of greater than 98% and approximating the optimal transfer fuel consumption with a relative estimation error of less than 0.4%. The tests on two asteroid chains further show the superiority of the DNN-based method for application to the design of low-thrust-based multitarget interplanetary missions
Tasks
Published	2019-02-11
URL	http://arxiv.org/abs/1902.03738v1
PDF	http://arxiv.org/pdf/1902.03738v1.pdf
PWC	https://paperswithcode.com/paper/fast-evaluation-of-low-thrust-transfers-via
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Stable and Fair Classification


Title	Stable and Fair Classification
Authors	Lingxiao Huang, Nisheeth K. Vishnoi
Abstract	Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset – a crucial consideration in several real-world applications. Motivated by their work, we study the problem of designing classification algorithms that are both fair and stable. We propose an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term. Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework. Our accuracy guarantee can be used to inform the selection of the regularization parameter in our framework. To the best of our knowledge, this is the first work that combines stability and fairness in automated decision-making tasks. We assess the benefits of our approach empirically by extending several fair classification algorithms that are shown to achieve the best balance between fairness and accuracy over the Adult dataset. Our empirical results show that our framework indeed improves the stability at only a slight sacrifice in accuracy.
Tasks	Decision Making
Published	2019-02-21
URL	https://arxiv.org/abs/1902.07823v3
PDF	https://arxiv.org/pdf/1902.07823v3.pdf
PWC	https://paperswithcode.com/paper/stable-and-fair-classification
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Evaluating Word Embedding Models: Methods and Experimental Results


Title	Evaluating Word Embedding Models: Methods and Experimental Results
Authors	Bin Wang, Angela Wang, Fenxiao Chen, Yuncheng Wang, C. -C. Jay Kuo
Abstract	Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evalutors.
Tasks	Word Embeddings
Published	2019-01-28
URL	http://arxiv.org/abs/1901.09785v2
PDF	http://arxiv.org/pdf/1901.09785v2.pdf
PWC	https://paperswithcode.com/paper/evaluating-word-embedding-models-methods-and
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A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery


Title	A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery
Authors	Zhexiong Shang, Chongsheng Cheng, Zhigang Shen
Abstract	UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent ground inspections can be scheduled. This two-phase inspection procedure showed great potentials on increasing field inspection productivity. Since aerial and ground inspection images are taken at different scales, a tool to properly fuse these multi-scale images is needed for improving the current bridge deck condition monitoring practice. In response to this need a data fusion platform is introduced in this study. Using this proposed platform multi-scale images taken by different inspection devices can be fused through geo-referencing. As part of the platform, a web-based user interface is developed to organize and visualize those images with inspection notes under users queries. For illustration purpose, a case study involving multi-scale optical and infrared images from UAV and ground inspector, and its implementation using the proposed platform is presented.
Tasks
Published	2019-04-10
URL	http://arxiv.org/abs/1904.04986v1
PDF	http://arxiv.org/pdf/1904.04986v1.pdf
PWC	https://paperswithcode.com/paper/a-data-fusion-platform-for-supporting-bridge
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