pwc-archive/papers-with-abstracts huggingface dataset:
MobileFAN: Transferring Deep Hidden Representation for Face Alignment
Authors: Yang Zhao, Yifan Liu, Chunhua Shen, Yongsheng Gao, Shengwu Xiong
Date: 2019-08-11 00:00:00
Tasks: Decoder, Face Alignment, Facial Landmark Detection

Facial landmark detection is a crucial prerequisite for many face analysis applications. Deep learning-based methods currently dominate the approach of addressing the facial landmark detection. However, such works generally introduce a large number of parameters, resulting in high memory cost. In this paper, we aim for a lightweight as well as effective solution to facial landmark detection. To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder. The proposed MobileFAN, with only 8% of the model size and lower computational cost, achieves superior or equivalent performance compared with state-of-the-art models. Moreover, by transferring the geometric structural information of a face graph from a large complex model to our proposed MobileFAN through feature-aligned distillation and feature-similarity distillation, the performance of MobileFAN is further improved in effectiveness and efficiency for face alignment. Extensive experiment results on three challenging facial landmark estimation benchmarks including COFW, 300W and WFLW show the superiority of our proposed MobileFAN against state-of-the-art methods.
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
Authors: Xinyu Gong, Shiyu Chang, Yifan Jiang, Zhangyang Wang
Date: 2019-08-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: image-classification, Image Classification, Image Generation, Neural Architecture Search, Unconditional Image Generation

Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy is introduced to perform NAS in a progressive way. Experiments validate the effectiveness of AutoGAN on the task of unconditional image generation. Specifically, our discovered architectures achieve highly competitive performance compared to current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also conclude with a discussion of the current limitations and future potential of AutoGAN. The code is available at https://github.com/TAMU-VITA/AutoGAN
Space-time error estimates for deep neural network approximations for differential equations
Authors: Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philipp Zimmermann
Date: 2019-08-11 00:00:00
Tasks: Articles, image-classification, Image Classification, speech-recognition, Speech Recognition

Over the last few years deep artificial neural networks (DNNs) have very successfully been used in numerical simulations for a wide variety of computational problems including computer vision, image classification, speech recognition, natural language processing, as well as computational advertisement. In addition, it has recently been proposed to approximate solutions of partial differential equations (PDEs) by means of stochastic learning problems involving DNNs. There are now also a few rigorous mathematical results in the scientific literature which provide error estimates for such deep learning based approximation methods for PDEs. All of these articles provide spatial error estimates for neural network approximations for PDEs but do not provide error estimates for the entire space-time error for the considered neural network approximations. It is the subject of the main result of this article to provide space-time error estimates for DNN approximations of Euler approximations of certain perturbed differential equations. Our proof of this result is based (i) on a certain artificial neural network (ANN) calculus and (ii) on ANN approximation results for products of the form $[0,T]\times \mathbb{R}^d\ni (t,x)\mapsto tx\in \mathbb{R}^d$ where $T\in (0,\infty)$, $d\in \mathbb{N}$, which we both develop within this article.
Supervised Negative Binomial Classifier for Probabilistic Record Linkage
Authors: Harish Kashyap K, Kiran Byadarhaly, Saumya Shah
Date: 2019-08-11 00:00:00
Tasks: Active Learning

Motivated by the need of the linking records across various databases, we propose a novel graphical model based classifier that uses a mixture of Poisson distributions with latent variables. The idea is to derive insight into each pair of hypothesis records that match by inferring its underlying latent rate of error using Bayesian Modeling techniques. The novel approach of using gamma priors for learning the latent variables along with supervised labels is unique and allows for active learning. The naive assumption is made deliberately as to the independence of the fields to propose a generalized theory for this class of problems and not to undermine the hierarchical dependencies that could be present in different scenarios. This classifier is able to work with sparse and streaming data. The application to record linkage is able to meet several challenges of sparsity, data streams and varying nature of the data-sets.
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Authors: Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang
Date: 2019-08-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Blind Face Restoration, Deblurring, Generative Adversarial Network, Image Deblurring, Image Restoration, Navigate, Single-Image Blind Deblurring, Video Deblurring

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a double-scale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plug-in of sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-of-the-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides, we show the architecture to be effective for general image restoration tasks too. Our codes, models and data are available at: https://github.com/KupynOrest/DeblurGANv2
Influence of Neighborhood on the Preference of an Item in eCommerce Search
Authors: Saratchandra Indrakanti, Svetlana Strunjas, Shubhangi Tandon, Manojkumar Rangasamy Kannadasan
Date: 2019-08-10 00:00:00
Tasks: Learning-To-Rank

Surfacing a ranked list of items for a search query to help buyers discover inventory and make purchase decisions is a critical problem in eCommerce search. Typically, items are independently predicted with a probability of sale with respect to a given search query. But in a dynamic marketplace like eBay, even for a single product, there are various different factors distinguishing one item from another which can influence the purchase decision for the user. Users have to make a purchase decision by considering all of these options. Majority of the existing learning to rank algorithms model the relative relevance between labeled items only at the loss functions like pairwise or list-wise losses. But they are limited to point-wise scoring functions where items are ranked independently based on the features of the item itself. In this paper, we study the influence of an item's neighborhood to its purchase decision. Here, we consider the neighborhood as the items ranked above and below the current item in search results. By adding delta features comparing items within a neighborhood and learning a ranking model, we are able to experimentally show that the new ranker with delta features outperforms our baseline ranker in terms of Mean Reciprocal Rank (MRR). The ranking models with proposed delta features result in $3-5\%$ improvement in MRR over the baseline model. We also study impact of different sizes for neighborhood. Experimental results show that neighborhood size $3$ perform the best based on MRR with an improvement of $4-5\%$ over the baseline model.
Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
Authors: Safa Alver, Ugur Halici
Date: 2019-08-10 00:00:00
Tasks: feature selection, Object Tracking, regression

Visual face tracking is one of the most important tasks in video surveillance systems. However, due to the variations in pose, scale, expression, and illumination it is considered to be a difficult task. Recent studies show that deep learning methods have a significant potential in object tracking tasks and adaptive feature selection methods can boost their performance. Motivated by these, we propose an end-to-end attentive deep learning based tracker, that is build on top of the state-of-the-art GOTURN tracker, for the task of real-time visual face tracking in video surveillance. Our method outperforms the state-of-the-art GOTURN and IVT trackers by very large margins and it achieves speeds that are very far beyond the requirements of real-time tracking. Additionally, to overcome the scarce data problem in visual face tracking, we also provide bounding box annotations for the G1 and G2 sets of ChokePoint dataset and make it suitable for further studies in face tracking under surveillance conditions.
Classical Information Theory of Networks
Authors: Filippo Radicchi, Dmitri Krioukov, Harrison Hartle, Ginestra Bianconi
Date: 2019-08-10 00:00:00

Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this gap of knowledge by developing a classical framework for networks based on finding an optimal trade-off between the information content of a compressed representation of the ensemble and the information content of the actual network ensemble. In this way not only we introduce a novel classical network ensemble satisfying a set of soft constraints but we are also able to calculate the optimal distribution of the constraints. We show that for the classical network ensemble in which the only constraints are the expected degrees a power-law degree distribution is optimal. Also, we study spatially embedded networks finding that the interactions between nodes naturally lead to non-uniform spread of nodes in the space, with pairs of nodes at a given distance not necessarily obeying a power-law distribution. The pertinent features of real-world air transportation networks are well described by the proposed framework.
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery
Authors: Jonathan Howe, Kyle Pula, Aaron A. Reite
Date: 2019-08-10 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection, Segmentation, vehicle detection

The difficulty in obtaining labeled data relevant to a given task is among the most common and well-known practical obstacles to applying deep learning techniques to new or even slightly modified domains. The data volumes required by the current generation of supervised learning algorithms typically far exceed what a human needs to learn and complete a given task. We investigate ways to expand a given labeled corpus of remote sensed imagery into a larger corpus using Generative Adversarial Networks (GANs). We then measure how these additional synthetic data affect supervised machine learning performance on an object detection task. Our data driven strategy is to train GANs to (1) generate synthetic segmentation masks and (2) generate plausible synthetic remote sensing imagery corresponding to these segmentation masks. Run sequentially, these GANs allow the generation of synthetic remote sensing imagery complete with segmentation labels. We apply this strategy to the data set from ISPRS' 2D Semantic Labeling Contest - Potsdam, with a follow on vehicle detection task. We find that in scenarios with limited training data, augmenting the available data with such synthetically generated data can improve detector performance.
Object-Aware Instance Labeling for Weakly Supervised Object Detection
Authors: Satoshi Kosugi, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2019-08-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

Weakly supervised object detection (WSOD), where a detector is trained with only image-level annotations, is attracting more and more attention. As a method to obtain a well-performing detector, the detector and the instance labels are updated iteratively. In this study, for more efficient iterative updating, we focus on the instance labeling problem, a problem of which label should be annotated to each region based on the last localization result. Instead of simply labeling the top-scoring region and its highly overlapping regions as positive and others as negative, we propose more effective instance labeling methods as follows. First, to solve the problem that regions covering only some parts of the object tend to be labeled as positive, we find regions covering the whole object focusing on the context classification loss. Second, considering the situation where the other objects contained in the image can be labeled as negative, we impose a spatial restriction on regions labeled as negative. Using these instance labeling methods, we train the detector on the PASCAL VOC 2007 and 2012 and obtain significantly improved results compared with other state-of-the-art approaches.
A Critical Note on the Evaluation of Clustering Algorithms
Authors: Tiantian Zhang, Li Zhong, Bo Yuan
Date: 2019-08-10 00:00:00
Tasks: Clustering, Dimensionality Reduction

Experimental evaluation is a major research methodology for investigating clustering algorithms and many other machine learning algorithms. For this purpose, a number of benchmark datasets have been widely used in the literature and their quality plays a key role on the value of the research work. However, in most of the existing studies, little attention has been paid to the properties of the datasets and they are often regarded as black-box problems. For example, it is common to use datasets intended for classification in clustering research and assume class la-bels as the ground truth for judging the quality of cluster-ing. In our work, with the help of advanced visualization and dimension reduction techniques, we show that this practice may seriously compromise the research quality and produce misleading results. We suggest that the applicability of existing benchmark datasets should be carefully revisited and significant efforts need to be devoted to improving the current practice of experimental evaluation of clustering algorithms to ensure an essential match between algorithms and problems.
A theory of incremental compression
Authors: Arthur Franz, Oleksandr Antonenko, Roman Soletskyi
Date: 2019-08-10 00:00:00

The ability to find short representations, i.e. to compress data, is crucial for many intelligent systems. We present a theory of incremental compression showing that arbitrary data strings, that can be described by a set of features, can be compressed by searching for those features incrementally, which results in a partition of the information content of the string into a complete set of pairwise independent pieces. The description length of this partition turns out to be close to optimal in terms of the Kolmogorov complexity of the string. Exploiting this decomposition, we introduce ALICE - a computable ALgorithm for Incremental ComprEssion - and derive an expression for its time complexity. Finally, we show that our concept of a feature is closely related to Martin-L\"of randomness tests, thereby formalizing the meaning of "property" for computable objects.
Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories
Authors: Mojtaba S. Fazli, Rachel V. Stadler, BahaaEddin Alaila, Stephen A. Vella, Silvia N. J. Moreno, Gary E. Ward, Shannon Quinn
Date: 2019-08-10 00:00:00
Tasks: Cell Detection, Cell Tracking, Clustering

Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in particular, toxoplasmosis is the disease caused by the parasite Toxoplasma gondii. Roughly, one-third of the world's population tests positive for T. gondii. Its virulence is linked to its lytic cycle, predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its motility patterns are critical to the eventual development of therapeutic strategies. Here, we present a computational framework for fast and scalable detection, tracking, and identification of T. gondii motion phenotypes in 3D videos, in a completely unsupervised fashion. Our pipeline consists of several different modules including preprocessing, sparsification, cell detection, cell tracking, trajectories extraction, parametrization of the trajectories; and finally, a clustering step. Additionally, we identified the computational bottlenecks, and developed a lightweight and highly scalable pipeline through a combination of task distribution and parallelism. Our results prove both the accuracy and performance of our method.
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
Authors: Subhabrata Dutta, Dipankar Das, Tanmoy Chakraborty
Date: 2019-08-10 00:00:00

Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems -- predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as `cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions. We employ four baselines -- first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement -- whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC.
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning
Authors: Xiaoqiang Wang, Liangjun Ke, Zhimin Qiao, Xinghua Chai
Date: 2019-08-10 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Finding the optimal signal timing strategy is a difficult task for the problem of large-scale traffic signal control (TSC). Multi-Agent Reinforcement Learning (MARL) is a promising method to solve this problem. However, there is still room for improvement in extending to large-scale problems and modeling the behaviors of other agents for each individual agent. In this paper, a new MARL, called Cooperative double Q-learning (Co-DQL), is proposed, which has several prominent features. It uses a highly scalable independent double Q-learning method based on double estimators and the UCB policy, which can eliminate the over-estimation problem existing in traditional independent Q-learning while ensuring exploration. It uses mean field approximation to model the interaction among agents, thereby making agents learn a better cooperative strategy. In order to improve the stability and robustness of the learning process, we introduce a new reward allocation mechanism and a local state sharing method. In addition, we analyze the convergence properties of the proposed algorithm. Co-DQL is applied on TSC and tested on a multi-traffic signal simulator. According to the results obtained on several traffic scenarios, Co- DQL outperforms several state-of-the-art decentralized MARL algorithms. It can effectively shorten the average waiting time of the vehicles in the whole road system.
Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM
Authors: Kyung Ho Park, EunJo Lee, Huy Kang Kim
Date: 2019-08-10 00:00:00
Tasks: Financial Analysis, Time Series, Time Series Analysis

With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and server-side. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavioral and social actions of character are analyzed with numerous algorithms. However, bot developers can evade the previous detection methods by changing bot's activities continuously. Eventually, overall maintenance cost increases because the selected features need to be updated along with the change of bot's behavior. To overcome this limitation, we propose improved bot detection method with financial analysis. As bot's activity absolutely necessitates the change of financial status, analyzing financial fluctuation effectively captures bots as a key feature. We trained and tested model with actual data of Aion, a leading MMORPG in Asia. Leveraging that LSTM efficiently recognizes time-series movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future.