October 18, 2019

3265 words 16 mins read

Paper Group ANR 517

2D Reconstruction of Small Intestine’s Interior Wall. Human Motion Modeling using DVGANs. Monotonic classification: an overview on algorithms, performance measures and data sets. Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders. Effective Learning of Probabilistic Models for Clinical Predicti …

2D Reconstruction of Small Intestine’s Interior Wall


Title	2D Reconstruction of Small Intestine’s Interior Wall
Authors	Rahman Attar, Xiang Xie, Zhihua Wang, Shigang Yue
Abstract	Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.
Tasks	Image Registration, Image Stitching
Published	2018-03-15
URL	http://arxiv.org/abs/1803.05817v1
PDF	http://arxiv.org/pdf/1803.05817v1.pdf
PWC	https://paperswithcode.com/paper/2d-reconstruction-of-small-intestines
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Human Motion Modeling using DVGANs


Title	Human Motion Modeling using DVGANs
Authors	Xiao Lin, Mohamed R. Amer
Abstract	We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization over actions, and generation of long diverse sequences. We evaluate our approach on Human 3.6M and CMU motion capture datasets using inception scores.
Tasks	Motion Capture
Published	2018-04-27
URL	http://arxiv.org/abs/1804.10652v2
PDF	http://arxiv.org/pdf/1804.10652v2.pdf
PWC	https://paperswithcode.com/paper/human-motion-modeling-using-dvgans
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Monotonic classification: an overview on algorithms, performance measures and data sets


Title	Monotonic classification: an overview on algorithms, performance measures and data sets
Authors	José-Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michał Woźniak, Salvador García
Abstract	Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfil restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field.
Tasks	Decision Making, Medical Diagnosis
Published	2018-11-17
URL	http://arxiv.org/abs/1811.07155v1
PDF	http://arxiv.org/pdf/1811.07155v1.pdf
PWC	https://paperswithcode.com/paper/monotonic-classification-an-overview-on
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Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders


Title	Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders
Authors	Jen J. Gong, Abigail Z. Jacobs, Toby E. Stuart, Mathijs de Vaan
Abstract	The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed with opioid use disorder, have diverse underlying health states. Policy interventions targeting prescription opioid use, opioid use disorder, and overdose often fail to account for this variation. To identify latent health states, or phenotypes, pertinent to opioid use and opioid use disorders, we use probabilistic topic modeling with medical diagnosis histories from a statewide population of individuals who were prescribed opioids. We demonstrate that our learned phenotypes are predictive of future opioid use-related outcomes. In addition, we show how the learned phenotypes can provide important context for variability in opioid prescriptions. Understanding the heterogeneity in individual health states and in prescription opioid use can help identify policy interventions to address this public health crisis.
Tasks	Medical Diagnosis
Published	2018-11-11
URL	http://arxiv.org/abs/1811.04344v3
PDF	http://arxiv.org/pdf/1811.04344v3.pdf
PWC	https://paperswithcode.com/paper/discovering-heterogeneous-subpopulations-for
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Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data


Title	Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data
Authors	Shuo Yang
Abstract	With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and value types caused by heterogeneous data sources; the uncertainty widely existing in every aspect of medical diagnosis and treatments; the high dimensionality of the feature space; the longitudinal medical records data with irregular intervals between adjacent observations; the richness of relations existing among objects with similar genetic factors, location or socio-demographic background. This thesis aims to develop advanced Statistical Relational Learning approaches in order to effectively exploit such health-related data and facilitate the discoveries in medical research. It presents the work on cost-sensitive statistical relational learning for mining structured imbalanced data, the first continuous-time probabilistic logic model for predicting sequential events from longitudinal structured data as well as hybrid probabilistic relational models for learning from heterogeneous structured data. It also demonstrates the outstanding performance of these proposed models as well as other state of the art machine learning models when applied to medical research problems and other real-world large-scale systems, reveals the great potential of statistical relational learning for exploring the structured health-related data to facilitate medical research.
Tasks	Medical Diagnosis, Relational Reasoning
Published	2018-11-02
URL	http://arxiv.org/abs/1811.00749v1
PDF	http://arxiv.org/pdf/1811.00749v1.pdf
PWC	https://paperswithcode.com/paper/effective-learning-of-probabilistic-models
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Domain Generalization via Conditional Invariant Representation


Title	Domain Generalization via Conditional Invariant Representation
Authors	Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao
Abstract	Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions. Let $X$ denote the features, and $Y$ be the class labels. Existing domain generalization methods address the dataset bias problem by learning a domain-invariant representation $h(X)$ that has the same marginal distribution $\mathbb{P}(h(X))$ across multiple source domains. The functional relationship encoded in $\mathbb{P}(YX)$ is usually assumed to be stable across domains such that $\mathbb{P}(Yh(X))$ is also invariant. However, it is unclear whether this assumption holds in practical problems. In this paper, we consider the general situation where both $\mathbb{P}(X)$ and $\mathbb{P}(YX)$ can change across all domains. We propose to learn a feature representation which has domain-invariant class conditional distributions $\mathbb{P}(h(X)Y)$. With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X),Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains. Extensive experiments on both synthetic and real data demonstrate the effectiveness of the proposed method.
Tasks	Domain Generalization
Published	2018-07-23
URL	http://arxiv.org/abs/1807.08479v1
PDF	http://arxiv.org/pdf/1807.08479v1.pdf
PWC	https://paperswithcode.com/paper/domain-generalization-via-conditional
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Deep learning in radiology: an overview of the concepts and a survey of the state of the art


Title	Deep learning in radiology: an overview of the concepts and a survey of the state of the art
Authors	Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
Abstract	Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.
Tasks
Published	2018-02-10
URL	http://arxiv.org/abs/1802.08717v1
PDF	http://arxiv.org/pdf/1802.08717v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-in-radiology-an-overview-of-the
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Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier


Title	Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier
Authors	Jordi de la Torre, Aida Valls, Domenec Puig, Pere Romero-Aroca
Abstract	Interpretability is a key factor in the design of automatic classifiers for medical diagnosis. Deep learning models have been proven to be a very effective classification algorithm when trained in a supervised way with enough data. The main concern is the difficulty of inferring rationale interpretations from them. Different attempts have been done in last years in order to convert deep learning classifiers from high confidence statistical black box machines into self-explanatory models. In this paper we go forward into the generation of explanations by identifying the independent causes that use a deep learning model for classifying an image into a certain class. We use a combination of Independent Component Analysis with a Score Visualization technique. In this paper we study the medical problem of classifying an eye fundus image into 5 levels of Diabetic Retinopathy. We conclude that only 3 independent components are enough for the differentiation and correct classification between the 5 disease standard classes. We propose a method for visualizing them and detecting lesions from the generated visual maps.
Tasks	Medical Diagnosis
Published	2018-09-23
URL	http://arxiv.org/abs/1809.08567v1
PDF	http://arxiv.org/pdf/1809.08567v1.pdf
PWC	https://paperswithcode.com/paper/identification-and-visualization-of-the
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Neural Network Topologies for Sparse Training


Title	Neural Network Topologies for Sparse Training
Authors	Ryan A. Robinett, Jeremy Kepner
Abstract	The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a sparse neural network. More recent work has demonstrated the remarkable result that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. An intriguing class of these sparse DNNs is the X-Nets, which are initialized and trained upon a sparse topology with neither reference to a parent dense DNN nor subsequent pruning. We present an algorithm that deterministically generates sparse DNN topologies that, as a whole, are much more diverse than X-Net topologies, while preserving X-Nets’ desired characteristics.
Tasks
Published	2018-09-14
URL	http://arxiv.org/abs/1809.05242v1
PDF	http://arxiv.org/pdf/1809.05242v1.pdf
PWC	https://paperswithcode.com/paper/neural-network-topologies-for-sparse-training
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Co-regularized Alignment for Unsupervised Domain Adaptation


Title	Co-regularized Alignment for Unsupervised Domain Adaptation
Authors	Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell
Abstract	Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks.
Tasks	Domain Adaptation, Unsupervised Domain Adaptation
Published	2018-11-13
URL	http://arxiv.org/abs/1811.05443v1
PDF	http://arxiv.org/pdf/1811.05443v1.pdf
PWC	https://paperswithcode.com/paper/co-regularized-alignment-for-unsupervised
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Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis


Title	Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis
Authors	Joao Reis, Gil Gonçalves
Abstract	Zero-shot learning (ZSL) can be defined by correctly solving a task where no training data is available, based on previous acquired knowledge from different, but related tasks. So far, this area has mostly drawn the attention from computer vision community where a new unseen image needs to be correctly classified, assuming the target class was not used in the training procedure. Apart from image classification, only a couple of generic methods were proposed that are applicable to both classification and regression. These learn the relation among model coefficients so new ones can be predicted according to provided conditions. So far, up to our knowledge, no methods exist that are applicable only to regression, and take advantage from such setting. Therefore, the present work proposes a novel algorithm for regression problems that uses data drawn from trained models, instead of model coefficients. In this case, a shape analyses on the data is performed to create a statistical shape model and generate new shapes to train new models. The proposed algorithm is tested in a theoretical setting using the beta distribution where main problem to solve is to estimate a function that predicts curves, based on already learned different, but related ones.
Tasks	Image Classification, Zero-Shot Learning
Published	2018-10-16
URL	http://arxiv.org/abs/1810.10330v1
PDF	http://arxiv.org/pdf/1810.10330v1.pdf
PWC	https://paperswithcode.com/paper/hyper-process-model-a-zero-shot-learning
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What can we learn from Semantic Tagging?


Title	What can we learn from Semantic Tagging?
Authors	Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, Lasha Abzianidze, Johan Bos
Abstract	We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
Tasks	Dependency Parsing, Multi-Task Learning, Natural Language Inference, Part-Of-Speech Tagging
Published	2018-08-29
URL	http://arxiv.org/abs/1808.09716v1
PDF	http://arxiv.org/pdf/1808.09716v1.pdf
PWC	https://paperswithcode.com/paper/what-can-we-learn-from-semantic-tagging
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Analysis of Network Lasso for Semi-Supervised Regression


Title	Analysis of Network Lasso for Semi-Supervised Regression
Authors	A. Jung, N. Vesselinova
Abstract	We apply network Lasso to semi-supervised regression problems involving network structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regression model, which is motivated by a clustering hypothesis, we provide an analysis of the estimation error incurred by network Lasso. This analysis reveals conditions on the the network structure and the available training data which guarantee network Lasso to be accurate. Remarkably, the accuracy of network Lasso is related to the existence of sufficiently large network flows over the empirical graph. Thus, our analysis reveals a connection between network Lasso and maximum flow problems.
Tasks
Published	2018-08-22
URL	http://arxiv.org/abs/1808.07249v2
PDF	http://arxiv.org/pdf/1808.07249v2.pdf
PWC	https://paperswithcode.com/paper/analysis-of-network-lasso-for-semi-supervised
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Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis


Title	Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Authors	Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak Lee
Abstract	We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches.
Tasks	Image Generation
Published	2018-01-16
URL	http://arxiv.org/abs/1801.05091v2
PDF	http://arxiv.org/pdf/1801.05091v2.pdf
PWC	https://paperswithcode.com/paper/inferring-semantic-layout-for-hierarchical
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Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation


Title	Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
Authors	Shiming Ge, Shengwei Zhao, Chenyu Li, Jia Li
Abstract	Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9,433 faces per second on GPU.
Tasks	Face Recognition
Published	2018-11-25
URL	http://arxiv.org/abs/1811.09998v2
PDF	http://arxiv.org/pdf/1811.09998v2.pdf
PWC	https://paperswithcode.com/paper/low-resolution-face-recognition-in-the-wild-1
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