pwc-archive/papers-with-abstracts huggingface dataset:
Short Text Language Identification for Under Resourced Languages
Authors: Bernardt Duvenhage
Date: 2019-11-18 00:00:00
Tasks: Language Identification

The paper presents a hierarchical naive Bayesian and lexicon based classifier for short text language identification (LID) useful for under resourced languages. The algorithm is evaluated on short pieces of text for the 11 official South African languages some of which are similar languages. The algorithm is compared to recent approaches using test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) shared tasks' datasets. Remaining research opportunities and pressing concerns in evaluating and comparing LID approaches are also discussed.
Multi-Task Learning of Height and Semantics from Aerial Images
Authors: Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Frédéric Champagnat, Andrés Almansa
Date: 2019-11-18 00:00:00
Tasks: Multi-Task Learning

Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network framework for learning semantics and local height together. We show how this joint multi-task learning benefits to each task on the large dataset of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map which allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images .
Finding Missing Children: Aging Deep Face Features
Authors: Debayan Deb, Divyansh Aggarwal, Anil K. Jain
Date: 2019-11-18 00:00:00
Tasks: Face Recognition

Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose an age-progression module that can age-progress deep face features output by any commodity face matcher. For time lapses larger than 10 years (the missing child is found after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of FaceNet from 40% to 49.56% and CosFace from 56.88% to 61.25% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate from 94.91% to 95.91% on a public aging dataset, FG-NET, and from 99.50% to 99.58% on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.
Fast Machine Learning with Byzantine Workers and Servers
Authors: El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis
Date: 2019-11-18 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) solutions are nowadays distributed and are prone to various types of component failures, which can be encompassed in so-called Byzantine behavior. This paper introduces LiuBei, a Byzantine-resilient ML algorithm that does not trust any individual component in the network (neither workers nor servers), nor does it induce additional communication rounds (on average), compared to standard non-Byzantine resilient algorithms. LiuBei builds upon gradient aggregation rules (GARs) to tolerate a minority of Byzantine workers. Besides, LiuBei replicates the parameter server on multiple machines instead of trusting it. We introduce a novel filtering mechanism that enables workers to filter out replies from Byzantine server replicas without requiring communication with all servers. Such a filtering mechanism is based on network synchrony, Lipschitz continuity of the loss function, and the GAR used to aggregate workers' gradients. We also introduce a protocol, scatter/gather, to bound drifts between models on correct servers with a small number of communication messages. We theoretically prove that LiuBei achieves Byzantine resilience to both servers and workers and guarantees convergence. We build LiuBei using TensorFlow, and we show that LiuBei tolerates Byzantine behavior with an accuracy loss of around 5% and around 24% convergence overhead compared to vanilla TensorFlow. We moreover show that the throughput gain of LiuBei compared to another state-of-the-art Byzantine-resilient ML algorithm (that assumes network asynchrony) is 70%.
Graph Neural Ordinary Differential Equations
Authors: Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park
Date: 2019-11-18 00:00:00

We introduce the framework of continuous--depth graph neural networks (GNNs). Graph neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs where the input-output relationship is determined by a continuum of GNN layers, blending discrete topological structures and differential equations. The proposed framework is shown to be compatible with various static and autoregressive GNN models. Results prove general effectiveness of GDEs: in static settings they offer computational advantages by incorporating numerical methods in their forward pass; in dynamic settings, on the other hand, they are shown to improve performance by exploiting the geometry of the underlying dynamics.
Ladder Loss for Coherent Visual-Semantic Embedding
Authors: Mo Zhou, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, Gang Hua
Date: 2019-11-18 00:00:00
Tasks: Retrieval, Triplet

For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way -- relevant or irrelevant, and all "irrelevant" candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. In particular, the new ladder loss is proposed by extending the triplet loss inequality to a more general inequality chain, which implements variable push-away margins according to respective relevance degrees. In addition, a proper Coherent Score metric is proposed to better measure the ranking results including those "irrelevant" candidates. Extensive experiments on multiple datasets validate the efficacy of our proposed method, which achieves significant improvement over existing state-of-the-art methods.
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
Authors: Yibo Yang, Hongyang Li, Xia Li, Qijie Zhao, Jianlong Wu, Zhouchen Lin
Date: 2019-11-18 00:00:00
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene graph representation, we formulate the overlapping problem as a simplified case, named scene overlap graph. We leverage each object's category, geometry and appearance features to perform relational embedding, and output a relation matrix that encodes overlap relations. In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances. The mask logits after removing overlaps are fed into per-pixel instance \verb|id| classification, which leverages the panoptic supervision to assist in the modeling of overlap relations. Besides, we generate an approximate ground truth of overlap relations as the weak supervision, to quantify the accuracy of overlap relations predicted by our method. Experiments on COCO and Cityscapes demonstrate that our method is able to accurately predict overlap relations, and outperform the state-of-the-art performance for panoptic segmentation. Our method also won the Innovation Award in COCO 2019 challenge.
The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
Authors: Junjie Huang, Zheng Zhu, Feng Guo, Guan Huang, Dalong Du
Date: 2019-11-18 00:00:00
Proceeding: CVPR 2020 6
Tasks: Pose Estimation

Being a fundamental component in training and inference, data processing has not been systematically considered in human pose estimation community, to the best of our knowledge. In this paper, we focus on this problem and find that the devil of human pose estimation evolution is in the biased data processing. Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including coordinate system transformation and keypoint format transformation (i.e., encoding and decoding), we find that the results obtained by common flipping strategy are unaligned with the original ones in inference. Moreover, there is a statistical error in some keypoint format transformation methods. Two problems couple together, significantly degrade the pose estimation performance and thus lay a trap for the research community. This trap has given bone to many suboptimal remedies, which are always unreported, confusing but influential. By causing failure in reproduction and unfair in comparison, the unreported remedies seriously impedes the technological development. To tackle this dilemma from the source, we propose Unbiased Data Processing (UDP) consist of two technique aspect for the two aforementioned problems respectively (i.e., unbiased coordinate system transformation and unbiased keypoint format transformation). As a model-agnostic approach and a superior solution, UDP successfully pushes the performance boundary of human pose estimation and offers a higher and more reliable baseline for research community. Code is public available in https://github.com/HuangJunJie2017/UDP-Pose
Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning
Authors: Ramtine Tofighi-Shirazi, Irina Mariuca Asavoae, Philippe Elbaz-Vincent
Date: 2019-11-18 00:00:00
Tasks: Ensemble Learning

The ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%.
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features
Authors: Tao Gui, Lizhi Qing, Qi Zhang, Jiacheng Ye, HangYan, Zichu Fei, Xuanjing Huang
Date: 2019-11-18 00:00:00
Tasks: Clustering, Data Augmentation, General Classification, image-classification, Image Classification, Meta-Learning, Multi-Task Learning

Multi-task learning (MTL) has received considerable attention, and numerous deep learning applications benefit from MTL with multiple objectives. However, constructing multiple related tasks is difficult, and sometimes only a single task is available for training in a dataset. To tackle this problem, we explored the idea of using unsupervised clustering to construct a variety of auxiliary tasks from unlabeled data or existing labeled data. We found that some of these newly constructed tasks could exhibit semantic meanings corresponding to certain human-specific attributes, but some were non-ideal. In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task. Experimental results across five image datasets demonstrated that the proposed method significantly outperformed existing single task learning, semi-supervised learning, and some data augmentation methods, including an improvement of more than 9% on the Omniglot dataset.
Automated Human Claustrum Segmentation using Deep Learning Technologies
Authors: Ahmed Awad Albishri, Syed Jawad Hussain Shah, Anthony Schmiedler, Seung Suk Kang, Yugyung Lee
Date: 2019-11-18 00:00:00
Tasks: Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

In recent years, Deep Learning (DL) has shown promising results in conducting AI tasks such as computer vision and image segmentation. Specifically, Convolutional Neural Network (CNN) models in DL have been applied to prevention,detection, and diagnosis in predictive medicine. Image segmentation plays a significant role in disease detection and prevention.However, there are enormous challenges in performing DL-based automatic segmentation due to the nature of medical images such as heterogeneous modalities and formats, insufficient labeled training data, and the high-class imbalance in the labeled data. Furthermore, automating segmentation of medical images,like magnetic resonance images (MRI), becomes a challenging task. The need for automated segmentation or annotation is what motivates our work. In this paper, we propose a fully automated approach that aims to segment the human claustrum for analytical purposes. We applied a U-Net CNN model to segment the claustrum (Cl) from a MRI dataset. With this approach, we have achieved an average Dice per case score of 0.72 for Cl segmentation, with K=5 for cross-validation. The expert in the medical domain also evaluates these results.
Benchmarking time series classification -- Functional data vs machine learning approaches
Authors: Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl
Date: 2019-11-18 00:00:00
Tasks: Additive models, Benchmarking, BIG-bench Machine Learning, Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal with the analysis of data that is continuously indexed over some domain. While often employing different methods, both fields strive to answer similar questions, a common example being classification or regression problems with functional covariates. We study methods from functional data analysis, such as functional generalized additive models, as well as functionality to concatenate (functional-) feature extraction or basis representations with traditional machine learning algorithms like support vector machines or classification trees. In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners. Additionally, we provide a software framework in R for functional data analysis for supervised learning, including machine learning and more linear approaches from statistics. This allows convenient access, and in connection with the machine-learning toolbox mlr, those methods can now also be tuned and benchmarked.
AI-based Pilgrim Detection using Convolutional Neural Networks
Authors: Marwa Ben Jabra, Adel Ammar, Anis Koubaa, Omar Cheikhrouhou, Habib Hamam
Date: 2019-11-18 00:00:00

Pilgrimage represents the most important Islamic religious gathering in the world where millions of pilgrims visit the holy places of Makkah and Madinah to perform their rituals. The safety and security of pilgrims is the highest priority for the authorities. In Makkah, 5000 cameras are spread around the holy for monitoring pilgrims, but it is almost impossible to track all events by humans considering the huge number of images collected every second. To address this issue, we propose to use artificial intelligence technique based on deep learning and convolution neural networks to detect and identify Pilgrims and their features. For this purpose, we built a comprehensive dataset for the detection of pilgrims and their genders. Then, we develop two convolutional neural networks based on YOLOv3 and Faster-RCNN for the detection of Pilgrims. Experiments results show that Faster RCNN with Inception v2 feature extractor provides the best mean average precision over all classes of 51%.
Safe squeezing for antisparse coding
Authors: Clément Elvira, Cédric Herzet
Date: 2019-11-18 00:00:00
Tasks: Dimensionality Reduction

Spreading the information over all coefficients of a representation is a desirable property in many applications such as digital communication or machine learning. This so-called antisparse representation can be obtained by solving a convex program involving an $\ell_\infty$-norm penalty combined with a quadratic discrepancy. In this paper, we propose a new methodology, dubbed safe squeezing, to accelerate the computation of antisparse representation. We describe a test that allows to detect saturated entries in the solution of the optimization problem. The contribution of these entries is compacted into a single vector, thus operating a form of dimensionality reduction. We propose two algorithms to solve the resulting lower dimensional problem. Numerical experiments show the effectiveness of the proposed method to detect the saturated components of the solution and illustrates the induced computational gains in the resolution of the antisparse problem.
Online Adaptive Asymmetric Active Learning with Limited Budgets
Authors: Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan
Date: 2019-11-18 00:00:00
Tasks: Active Learning, Anomaly Detection

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is quite difficult to handle imbalanced unlabeled datastream when only a limited budget of labels can be queried for training. To solve this, previous OAL studies adopt either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and use first-order methods to optimize the cost-sensitive measure. However, the isolated strategy limits their performance in class imbalance, while first-order methods restrict their optimization performance. In this paper, we propose a novel Online Adaptive Asymmetric Active learning algorithm, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its mistake bound and cost-sensitive metric bounds. Moreover, to better balance performance and efficiency, we enhance our algorithm via a sketching technique, which significantly accelerates the computational speed with quite slight performance degradation. Promising results demonstrate the effectiveness and efficiency of the proposed methods.
Towards Making Deep Transfer Learning Never Hurt
Authors: Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan
Date: 2019-11-18 00:00:00
Tasks: All, Knowledge Distillation, Transfer Learning

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%--7% higher accuracy in all experiments.
Fine-Grained Neural Architecture Search
Authors: Heewon Kim, Seokil Hong, Bohyung Han, Heesoo Myeong, Kyoung Mu Lee
Date: 2019-11-18 00:00:00
Tasks: image-classification, Image Classification, Image Super-Resolution, Neural Architecture Search, Super-Resolution

We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature maps using several different base operations. FGNAS runs efficiently in spite of significantly large search space compared to other methods because it trains networks end-to-end by a stochastic gradient descent method. Moreover, the proposed framework allows to optimize the network under predefined resource constraints in terms of number of parameters, FLOPs and latency. FGNAS has been applied to two crucial applications in resource demanding computer vision tasks---large-scale image classification and image super-resolution---and demonstrates the state-of-the-art performance through flexible operation search and channel pruning.
Deep and Dense Sarcasm Detection
Authors: Devin Pelser, Hugh Murrell
Date: 2019-11-18 00:00:00
Tasks: Sarcasm Detection

Recent work in automated sarcasm detection has placed a heavy focus on context and meta-data. Whilst certain utterances indeed require background knowledge and commonsense reasoning, previous works have only explored shallow models for capturing the lexical, syntactic and semantic cues present within a text. In this paper, we propose a deep 56 layer network, implemented with dense connectivity to model the isolated utterance and extract richer features therein. We compare our approach against recent state-of-the-art architectures which make considerable use of extrinsic information, and demonstrate competitive results whilst using only the local features of the text. Further, we provide an analysis of the dependency of prior convolution outputs in generating the final feature maps. Finally a case study is presented, supporting that our approach accurately classifies additional uses of clear sarcasm, which a standard CNN misclassifies.