pwc-archive/papers-with-abstracts huggingface dataset:
FOOD: Fast Out-Of-Distribution Detector
Authors: Guy Amit, Moshe Levy, Ishai Rosenberg, Asaf Shabtai, Yuval Elovici
Date: 2020-08-16 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs. However, out-of-distribution (OOD) inputs pose a great challenge to DNNs and consequently represent a major risk when DNNs are implemented in safety-critical systems. Extensive research has been performed in the domain of OOD detection. However, current state-of-the-art methods for OOD detection suffer from at least one of the following limitations: (1) increased inference time - this limits existing methods' applicability to many real-world applications, and (2) the need for OOD training data - such data can be difficult to acquire and may not be representative enough, thus limiting the ability of the OOD detector to generalize. In this paper, we propose FOOD -- Fast Out-Of-Distribution detector -- an extended DNN classifier capable of efficiently detecting OOD samples with minimal inference time overhead. Our architecture features a DNN with a final Gaussian layer combined with the log likelihood ratio statistical test and an additional output neuron for OOD detection. Instead of using real OOD data, we use a novel method to craft artificial OOD samples from in-distribution data, which are used to train our OOD detector neuron. We evaluate FOOD's detection performance on the SVHN, CIFAR-10, and CIFAR-100 datasets. Our results demonstrate that in addition to achieving state-of-the-art performance, FOOD is fast and applicable to real-world applications.
SGG: Spinbot, Grammarly and GloVe based Fake News Detection
Authors: Akansha Gautam, Koteswar Rao Jerripothula
Date: 2020-08-16 00:00:00
Tasks: Fake News Detection, Misinformation

Recently, news consumption using online news portals has increased exponentially due to several reasons, such as low cost and easy accessibility. However, such online platforms inadvertently also become the cause of spreading false information across the web. They are being misused quite frequently as a medium to disseminate misinformation and hoaxes. Such malpractices call for a robust automatic fake news detection system that can keep us at bay from such misinformation and hoaxes. We propose a robust yet simple fake news detection system, leveraging the tools for paraphrasing, grammar-checking, and word-embedding. In this paper, we try to the potential of these tools in jointly unearthing the authenticity of a news article. Notably, we leverage Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for word-embedding) tools for this purpose. Using these tools, we were able to extract novel features that could yield state-of-the-art results on the Fake News AMT dataset and comparable results on Celebrity datasets when combined with some of the essential features. More importantly, the proposed method is found to be more robust empirically than the existing ones, as revealed in our cross-domain analysis and multi-domain analysis.
Survey: Geometric Foundations of Data Reduction
Authors: Ce Ju
Date: 2020-08-16 00:00:00
Tasks: Dimensionality Reduction, Survey

This survey is written in summer, 2016. The purpose of this survey is to briefly introduce nonlinear dimensionality reduction (NLDR) in data reduction. The first two NLDR were respectively published in Science in 2000 in which they solve the similar reduction problem of high-dimensional data endowed with the intrinsic nonlinear structure. The intrinsic nonlinear structure is always interpreted as a concept in manifolds from geometry and topology in theoretical mathematics by computer scientists and theoretical physicists. In 2001, the concept of Manifold Learning first appears as an NLDR method called Laplacian Eigenmaps. In a typical manifold learning setup, the data set, also called the observation set, is distributed on or near a low dimensional manifold M embedded in RD, which yields that each observation has a D-dimensional representation. The goal of manifold learning is to reduce these observations as a compact lower-dimensional representation based on the geometric information. The reduction procedure is called the spectral manifold learning. In this paper, we derive each spectral manifold learning with the matrix and operator representation, and we then discuss the convergence behavior of each method in a geometric uniform language. Hence, the survey is named Geometric Foundations of Data Reduction.
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Authors: Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang
Date: 2020-08-16 00:00:00

Reinforcement learning is a powerful tool to learn the optimal policy of possibly multiple agents by interacting with the environment. As the number of agents grow to be very large, the system can be approximated by a mean-field problem. Therefore, it has motivated new research directions for mean-field control (MFC) and mean-field game (MFG). In this paper, we study the policy gradient method for the linear-quadratic mean-field control and game, where we assume each agent has identical linear state transitions and quadratic cost functions. While most of the recent works on policy gradient for MFC and MFG are based on discrete-time models, we focus on the continuous-time models where some analyzing techniques can be interesting to the readers. For both MFC and MFG, we provide policy gradient update and show that it converges to the optimal solution at a linear rate, which is verified by a synthetic simulation. For MFG, we also provide sufficient conditions for the existence and uniqueness of the Nash equilibrium.
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
Authors: Yuxiang Wei, Ming Liu, Haolin Wang, Ruifeng Zhu, Guosheng Hu, WangMeng Zuo
Date: 2020-08-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: Face Generation, Image Generation

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training. We propose a novel Flow-based Feature Warping Model (FFWM) which can learn to synthesize photo-realistic and illumination preserving frontal images with illumination inconsistent supervision. Specifically, an Illumination Preserving Module (IPM) is proposed to learn illumination preserving image synthesis from illumination inconsistent image pairs. IPM includes two pathways which collaborate to ensure the synthesized frontal images are illumination preserving and with fine details. Moreover, a Warp Attention Module (WAM) is introduced to reduce the pose discrepancy in the feature level, and hence to synthesize frontal images more effectively and preserve more details of profile images. The attention mechanism in WAM helps reduce the artifacts caused by the displacements between the profile and the frontal images. Quantitative and qualitative experimental results show that our FFWM can synthesize photo-realistic and illumination preserving frontal images and performs favorably against the state-of-the-art results.
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price
Authors: Zhiwen Zeng, Matloob Khushi
Date: 2020-08-16 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

Every change of trend in the forex market presents a great opportunity as well as a risk for investors. Accurate forecasting of forex prices is a crucial element in any effective hedging or speculation strategy. However, the complex nature of the forex market makes the predicting problem challenging, which has prompted extensive research from various academic disciplines. In this paper, a novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) are proposed. Wavelet transform removes the noise from the time series to stabilize the data structure. ARNN model captures the robust and non-linear relationships in the sequence and ARIMA can well fit the linear correlation of the sequential information. By hybridization of the three models, the methodology is capable of modelling dynamic systems such as the forex market. Our experiments on USD/JPY five-minute data outperforms the baseline methods. Root-Mean-Squared-Error (RMSE) of the hybrid approach was found to be 1.65 with a directional accuracy of ~76%.
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
Authors: Rui Fan, Hengli Wang, Mohammud J. Bocus, Ming Liu
Date: 2020-08-16 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the individual experience. Our recently introduced disparity (or inverse depth) transformation algorithm allows better discrimination between damaged and undamaged road areas, and it can be easily deployed to any semantic segmentation network for better road pothole detection results. To boost the performance, we propose a novel attention aggregation (AA) framework, which takes the advantages of different types of attention modules. In addition, we develop an effective training set augmentation technique based on adversarial domain adaptation, where the synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks. The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.
Open source tools for management and archiving of digital microscopy data to allow integration with patient pathology and treatment information
Authors: Matloob Khushi, Georgina Edwards, Diego Alonso de Marcos, Jane E Carpenter, J Dinny Graham, Christine L Clarke
Date: 2020-08-16 00:00:00
Tasks: Management

Virtual microscopy includes digitisation of histology slides and the use of computer technologies for complex investigation of diseases such as cancer. However, automated image analysis, or website publishing of such digital images, is hampered by their large file sizes. We have developed two Java based open source tools: Snapshot Creator and NDPI-Splitter. Snapshot Creator converts a portion of a large digital slide into a desired quality JPEG image. The image is linked to the patients clinical and treatment information in a customised open source cancer data management software (Caisis) in use at the Australian Breast Cancer Tissue Bank (ABCTB) and then published on the ABCTB website www.abctb.org.au using Deep Zoom open source technology. Using the ABCTB online search engine, digital images can be searched by defining various criteria such as cancer type, or biomarkers expressed. NDPI-Splitter splits a large image file into smaller sections of TIFF images so that they can be easily analysed by image analysis software such as Metamorph or Matlab. NDPI-Splitter also has the capacity to filter out empty images. Snapshot Creator and NDPI-Splitter are novel open source Java tools. They convert digital slides into files of smaller size for further processing. In conjunction with other open source tools such as Deep Zoom and Caisis, this suite of tools is used for the management and archiving of digital microscopy images, enabling digitised images to be explored and zoomed online. Our online image repository also has the capacity to be used as a teaching resource. These tools also enable large files to be sectioned for image analysis.
Differentially Private Multi-Agent Planning for Logistic-like Problems
Authors: Dayong Ye, Tianqing Zhu, Sheng Shen, Wanlei Zhou, Philip S. Yu
Date: 2020-08-16 00:00:00
Tasks: Privacy Preserving

Planning is one of the main approaches used to improve agents' working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private information leaked. This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. This approach outperforms existing approaches by addressing two challenges: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. These two challenges are prevalent in many real-world applications including logistics in military environments and packet routing in networks. To tackle these two challenges, our approach adopts the differential privacy technique, which can both guarantee strong privacy and control communication overhead. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems. We theoretically prove the strong privacy and completeness of our approach and empirically demonstrate its efficiency. We also theoretically analyze the communication overhead of our approach and illustrate how differential privacy can be used to control it.
DeepSampling: Selectivity Estimation with Predicted Error and Response Time
Authors: Tin Vu, Ahmed Eldawy
Date: 2020-08-16 00:00:00

The rapid growth of spatial data urges the research community to find efficient processing techniques for interactive queries on large volumes of data. Approximate Query Processing (AQP) is the most prominent technique that can provide real-time answer for ad-hoc queries based on a random sample. Unfortunately, existing AQP methods provide an answer without providing any accuracy metrics due to the complex relationship between the sample size, the query parameters, the data distribution, and the result accuracy. This paper proposes DeepSampling, a deep-learning-based model that predicts the accuracy of a sample-based AQP algorithm, specially selectivity estimation, given the sample size, the input distribution, and query parameters. The model can also be reversed to measure the sample size that would produce a desired accuracy. DeepSampling is the first system that provides a reliable tool for existing spatial databases to control the accuracy of AQP.
A novel approach to remove foreign objects from chest X-ray images
Authors: Hieu X. Le, Phuong D. Nguyen, Thang H. Nguyen, Khanh N. Q. Le, Thanh T. Nguyen
Date: 2020-08-16 00:00:00
Tasks: Diagnostic, Object, object-detection, Object Detection

We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic prediction are captured under various settings. In this paper, we used multi-method to tackle both removal and inpainting chest radiographs. Firstly, an object detection model is trained to separate the foreign objects from the given image. Subsequently, the binary mask of each object is extracted utilizing a segmentation model. Each pair of the binary mask and the extracted object are then used for inpainting purposes. Finally, the in-painted regions are now merged back to the original image, resulting in a clean and non-foreign-object-existing output. To conclude, we achieved state-of-the-art accuracy. The experimental results showed a new approach to the possible applications of this method for chest X-ray images detection.
Faster Person Re-Identification
Authors: Guan'an Wang, Shaogang Gong, Jian Cheng, Zeng-Guang Hou
Date: 2020-08-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: Code Search, Person Re-Identification, Playing the Game of 2048

Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a $F_{\beta}$ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8% more accurate but also 5x faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is $50\times$ faster with comparable accuracy. Code is available at https://github.com/wangguanan/light-reid.
Conjunctive Queries: Unique Characterizations and Exact Learnability
Authors: Balder ten Cate, Victor Dalmau
Date: 2020-08-16 00:00:00

We answer the question which conjunctive queries are uniquely characterized by polynomially many positive and negative examples, and how to construct such examples efficiently. As a consequence, we obtain a new efficient exact learning algorithm for a class of conjunctive queries. At the core of our contributions lie two new polynomial-time algorithms for constructing frontiers in the homomorphism lattice of finite structures. We also discuss implications for the unique characterizability and learnability of schema mappings and of description logic concepts.
Relevance Attack on Detectors
Authors: Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang
Date: 2020-08-16 00:00:00
Tasks: Autonomous Driving, Diversity, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures. To pursue a high attack transferability, one plausible way is to find a common property across detectors, which facilitates the discovery of common weaknesses. We are the first to suggest that the relevance map from interpreters for detectors is such a property. Based on it, we design a Relevance Attack on Detectors (RAD), which achieves a state-of-the-art transferability, exceeding existing results by above 20%. On MS COCO, the detection mAPs for all 8 black-box architectures are more than halved and the segmentation mAPs are also significantly influenced. Given the great transferability of RAD, we generate the first adversarial dataset for object detection and instance segmentation, i.e., Adversarial Objects in COntext (AOCO), which helps to quickly evaluate and improve the robustness of detectors.
Cascaded channel pruning using hierarchical self-distillation
Authors: Roy Miles, Krystian Mikolajczyk
Date: 2020-08-16 00:00:00
Tasks: Knowledge Distillation, Model Compression

In this paper, we propose an approach for filter-level pruning with hierarchical knowledge distillation based on the teacher, teaching-assistant, and student framework. Our method makes use of teaching assistants at intermediate pruning levels that share the same architecture and weights as the target student. We propose to prune each model independently using the gradient information from its corresponding teacher. By considering the relative sizes of each student-teacher pair, this formulation provides a natural trade-off between the capacity gap for knowledge distillation and the bias of the filter saliency updates. Our results show improvements in the attainable accuracy and model compression across the CIFAR10 and ImageNet classification tasks using the VGG16and ResNet50 architectures. We provide an extensive evaluation that demonstrates the benefits of using a varying number of teaching assistant models at different sizes.
Cluster-level Feature Alignment for Person Re-identification
Authors: Qiuyu Chen, Wei zhang, Jianping Fan
Date: 2020-08-15 00:00:00
Tasks: Person Re-Identification, Triplet

Instance-level alignment is widely exploited for person re-identification, e.g. spatial alignment, latent semantic alignment and triplet alignment. This paper probes another feature alignment modality, namely cluster-level feature alignment across whole dataset, where the model can see not only the sampled images in local mini-batch but the global feature distribution of the whole dataset from distilled anchors. Towards this aim, we propose anchor loss and investigate many variants of cluster-level feature alignment, which consists of iterative aggregation and alignment from the overview of dataset. Our extensive experiments have demonstrated that our methods can provide consistent and significant performance improvement with small training efforts after the saturation of traditional training. In both theoretical and experimental aspects, our proposed methods can result in more stable and guided optimization towards better representation and generalization for well-aligned embedding.
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition
Authors: Henry Tsai, Jayden Ooi, Chun-Sung Ferng, Hyung Won Chung, Jason Riesa
Date: 2020-08-15 00:00:00
Tasks: Neural Architecture Search

Transformer-based models have achieved stateof-the-art results in many tasks in natural language processing. However, such models are usually slow at inference time, making deployment difficult. In this paper, we develop an efficient algorithm to search for fast models while maintaining model quality. We describe a novel approach to decompose the Transformer architecture into smaller components, and propose a sampling-based one-shot architecture search method to find an optimal model for inference. The model search process is more efficient than alternatives, adding only a small overhead to training time. By applying our methods to BERT-base architectures, we achieve 10% to 30% speedup for pre-trained BERT and 70% speedup on top of a previous state-of-the-art distilled BERT model on Cloud TPU-v2 with a generally acceptable drop in performance.
Chrome Dino Run using Reinforcement Learning
Authors: Divyanshu Marwah, Sneha Srivastava, Anusha Gupta, Shruti Verma
Date: 2020-08-15 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.