pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images
Authors: Sambuddha Ghosal, Pratik Shah
Date: 2020-11-11 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Transfer Learning

Deep learning (DL) models for disease classification or segmentation from medical images are increasingly trained using transfer learning (TL) from unrelated natural world images. However, shortcomings and utility of TL for specialized tasks in the medical imaging domain remain unknown and are based on assumptions that increasing training data will improve performance. We report detailed comparisons, rigorous statistical analysis and comparisons of widely used DL architecture for binary segmentation after TL with ImageNet initialization (TII-models) with supervised learning with only medical images(LMI-models) of macroscopic optical skin cancer, microscopic prostate core biopsy and Computed Tomography (CT) DICOM images. Through visual inspection of TII and LMI model outputs and their Grad-CAM counterparts, our results identify several counter intuitive scenarios where automated segmentation of one tumor by both models or the use of individual segmentation output masks in various combinations from individual models leads to 10% increase in performance. We also report sophisticated ensemble DL strategies for achieving clinical grade medical image segmentation and model explanations under low data regimes. For example; estimating performance, explanations and replicability of LMI and TII models described by us can be used for situations in which sparsity promotes better learning. A free GitHub repository of TII and LMI models, code and more than 10,000 medical images and their Grad-CAM output from this study can be used as starting points for advanced computational medicine and DL research for biomedical discovery and applications.
Assessment of text coherence based on the cohesion estimation
Authors: S. D. Pogorilyy, A. A. Kramov
Date: 2020-11-11 00:00:00

In this paper, a graph-based coherence estimation method based on the cohesion estimation is suggested. Our method uses a graph-based approach to provide a user with an understanding of the evaluation process. Moreover, it can be applied to different languages, therefore, the effectiveness of this method is examined on the set of English, Chinese, and Arabic texts.
Learned Equivariant Rendering without Transformation Supervision
Authors: Cinjon Resnick, Or Litany, Hugo Larochelle, Joan Bruna, Kyunghyun Cho
Date: 2020-11-11 00:00:00

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background. Our method relies on moving objects being equivariant with respect to their transformation across frames and the background being constant. After training, we can manipulate and render the scenes in real time to create unseen combinations of objects, transformations, and backgrounds. We show results on moving MNIST with backgrounds.
Fast semidefinite programming with feedforward neural networks
Authors: Tamás Kriváchy, Yu Cai, Joseph Bowles, Daniel Cavalcanti, Nicolas Brunner
Date: 2020-11-11 00:00:00

Semidefinite programming is an important optimization task, often used in time-sensitive applications. Though they are solvable in polynomial time, in practice they can be too slow to be used in online, i.e. real-time applications. Here we propose to solve feasibility semidefinite programs using artificial neural networks. Given the optimization constraints as an input, a neural network outputs values for the optimization parameters such that the constraints are satisfied, both for the primal and the dual formulations of the task. We train the network without having to exactly solve the semidefinite program even once, thus avoiding the possibly time-consuming task of having to generate many training samples with conventional solvers. The neural network method is only inconclusive if both the primal and dual models fail to provide feasible solutions. Otherwise we always obtain a certificate, which guarantees false positives to be excluded. We examine the performance of the method on a hierarchy of quantum information tasks, the Navascu\'es-Pironio-Ac\'in hierarchy applied to the Bell scenario. We demonstrate that the trained neural network gives decent accuracy, while showing orders of magnitude increase in speed compared to a traditional solver.
Generative and Discriminative Learning for Distorted Image Restoration
Authors: Yi Gu, Yuting Gao, Jie Li, Chentao Wu, Weijia Jia
Date: 2020-11-11 00:00:00
Tasks: Image Restoration

Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task. To edit images in an efficient way, distorted images are expected to be restored automatically. This paper aims at the distorted image restoration, which is characterized by seeking the appropriate warping and completion of a distorted image. Existing methods focus on the hardware assistance or the geometric principle to solve the specific regular deformation caused by natural phenomena, but they cannot handle the irregularity and uncertainty of artificial distortion in this task. To address this issue, we propose a novel generative and discriminative learning method based on deep neural networks, which can learn various reconstruction mappings and represent complex and high-dimensional data. This method decomposes the task into a rectification stage and a refinement stage. The first stage generative network predicts the mapping from the distorted images to the rectified ones. The second stage generative network then further optimizes the perceptual quality. Since there is no available dataset or benchmark to explore this task, we create a Distorted Face Dataset (DFD) by forward distortion mapping based on CelebA dataset. Extensive experimental evaluation on the proposed benchmark and the application demonstrates that our method is an effective way for distorted image restoration.
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks
Authors: Pierre Aumjaud, David McAuliffe, Francisco Javier Rodríguez Lera, Philip Cardiff
Date: 2020-11-11 00:00:00
Tasks: Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training. In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms. In this paper, we define a robust, reproducible and systematic experimental procedure to compare the performance of various model-free algorithms at solving this task. The policies are trained in simulation and are then transferred to a physical robotic manipulator. It is shown that augmenting the reward signal with the Hindsight Experience Replay exploration technique increases the average return of off-policy agents between 7 and 9 folds when the target position is initialised randomly at the beginning of each episode.
The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic
Authors: Nicholas Micallef, Bing He, Srijan Kumar, Mustaque Ahamad, Nasir Memon
Date: 2020-11-11 00:00:00
Tasks: Fact Checking, Misinformation

Fact checking by professionals is viewed as a vital defense in the fight against misinformation.While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation. To explore if this is the case, we conduct a data-driven study of misinformation on the Twitter platform, focusing on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowd response to popular misleading claims about COVID-19. In this work, we curate a dataset of false claims and statements that seek to challenge or refute them. We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments.Our findings show that professional fact-checking tweets have limited volume and reach. In contrast, we observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation. More importantly, we find contrasting differences in the way the crowd refutes tweets, some tweets appear to be opinions, while others contain concrete evidence, such as a link to a reputed source. Our work provides insights into how misinformation is organically countered in social platforms by some of their users and the role they play in amplifying professional fact checks.These insights could lead to development of tools and mechanisms that can empower concerned citizens in combating misinformation. The code and data can be found in http://claws.cc.gatech.edu/covid_counter_misinformation.html.
Finding Relevant Flood Images on Twitter using Content-based Filters
Authors: Björn Barz, Kai Schröter, Ann-Christin Kra, Joachim Denzler
Date: 2020-11-11 00:00:00

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abundance of images of the event posted on social media platforms such as Twitter. These images could be used to document and rapidly assess the situation and derive proxy-data not available from sensors, e.g., the degree of water pollution. However, not all images posted online are suitable or informative enough for this purpose. Therefore, we propose an automatic filtering approach using machine learning techniques for finding Twitter images that are relevant for one of the following information objectives: assessing the flooded area, the inundation depth, and the degree of water pollution. Instead of relying on textual information present in the tweet, the filter analyzes the image contents directly. We evaluate the performance of two different approaches and various features on a case-study of two major flooding events. Our image-based filter is able to enhance the quality of the results substantially compared with a keyword-based filter, improving the mean average precision from 23% to 53% on average.
End-To-End Semi-supervised Learning for Differentiable Particle Filters
Authors: Hao Wen, Xiongjie Chen, Georgios Papagiannis, Conghui Hu, Yunpeng Li
Date: 2020-11-11 00:00:00
Tasks: State Estimation

Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic and measurement models in this framework are learnable through the differentiable implementation of particle filters. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. In this paper, in order to reduce the demand for annotated data, we present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown. We assess performance of the proposed method in state estimation tasks in robotics with simulated and real-world datasets.
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines
Authors: Umut Özkaya, Şaban Öztürk, Serkan Budak, Farid Melgani, Kemal Polat
Date: 2020-11-11 00:00:00
Tasks: Computed Tomography (CT), General Classification, Transfer Learning

Purpose: Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. Methods: In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training (%75) and testing (%25). The CSVM model consists of blocks containing three different numbers of SVM kernels. Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values. Conclusion: The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
Authors: Yitao Cai, Xiaojun Wan
Date: 2020-11-11 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Text to SQL, Text-To-SQL

Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historical user inputs. In this work, in addition to using encoders to capture historical information of user inputs, we propose a database schema interaction graph encoder to utilize historicalal information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.
Learning User Representations with Hypercuboids for Recommender Systems
Authors: Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou
Date: 2020-11-11 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is that it explicitly models user interests as a hypercuboid instead of a point in the space. In our approach, the recommendation score is learned by calculating a compositional distance between the user hypercuboid and the item. This helps to alleviate the potential geometric inflexibility of existing collaborative filtering approaches, enabling a greater extent of modeling capability. Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests. A neural architecture is also proposed to facilitate user hypercuboid learning by capturing the activity sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of our proposed model via extensive experiments on both public and commercial datasets. Empirical results show that our approach achieves very promising results, outperforming existing state-of-the-art.
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Authors: Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama, Mario Ynocente Castro, Karim Hamzaoui, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
Date: 2020-11-11 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic participants, tuning these models to increase their behavioral diversity while maintaining quality is often very challenging. This paper introduces an easily-tunable policy generation algorithm for autonomous driving agents. The proposed algorithm balances diversity and driving skills by leveraging the representation and exploration abilities of deep reinforcement learning via a distinct policy set selector. Moreover, we present an algorithm utilizing intrinsic rewards to widen behavioral differences in the training. To provide quantitative assessments, we develop two trajectory-based evaluation metrics which measure the differences among policies and behavioral coverage. We experimentally show the effectiveness of our methods on several challenging intersection scenes.
Survey on 3D face reconstruction from uncalibrated images
Authors: Araceli Morales, Gemma Piella, Federico M. Sukno
Date: 2020-11-11 00:00:00
Tasks: 3D Face Reconstruction, Deep Learning, Face Reconstruction, Survey

Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a list of the most popular publicly available 3D facial models. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, becoming the standard choice in replacement of the widespread statistical model fitting. Unlike the other two strategies, photometry-based methods have decreased in number due to the need for strong underlying assumptions that limit the quality of their reconstructions compared to statistical model fitting and deep learning methods. The review also identifies current challenges and suggests avenues for future research.
DeepSim: Semantic similarity metrics for learned image registration
Authors: Steffen Czolbe, Oswin Krause, Aasa Feragen
Date: 2020-11-11 00:00:00
Tasks: Image Registration, Semantic Similarity, Semantic Textual Similarity

We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our semantic approach learns dataset-specific features that drive the optimization of a learning-based registration model. Comparing to existing unsupervised and supervised methods across multiple image modalities and applications, we achieve consistently high registration accuracy and faster convergence than state of the art, and the learned invariance to noise gives smoother transformations on low-quality images.
A CNN-based Feature Space for Semi-supervised Incremental Learning in Assisted Living Applications
Authors: Tobias Scheck, Ana Perez Grassi, Gangolf Hirtz
Date: 2020-11-11 00:00:00
Tasks: General Classification, Incremental Learning

A Convolutional Neural Network (CNN) is sometimes confronted with objects of changing appearance ( new instances) that exceed its generalization capability. This requires the CNN to incorporate new knowledge, i.e., to learn incrementally. In this paper, we are concerned with this problem in the context of assisted living. We propose using the feature space that results from the training dataset to automatically label problematic images that could not be properly recognized by the CNN. The idea is to exploit the extra information in the feature space for a semi-supervised labeling and to employ problematic images to improve the CNN's classification model. Among other benefits, the resulting semi-supervised incremental learning process allows improving the classification accuracy of new instances by 40% as illustrated by extensive experiments.
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Authors: Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Daxin Jiang
Date: 2020-11-11 00:00:00
Tasks: Boundary Detection, Cross-Lingual NER, Machine Reading Comprehension, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Reading Comprehension

Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC). One major obstacle is the errors on the boundary of predicted answers. To tackle this problem, we propose CalibreNet, which predicts answers in two steps. In the first step, any existing sequence labeling method can be adopted as a base model to generate an initial answer. In the second step, CalibreNet refines the boundary of the initial answer. To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet. Experiments on two cross-lingual benchmark datasets show that the proposed approach achieves SOTA results on zero-shot cross-lingual NER and MRC tasks.
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Authors: Tobias Scheck, Roman Seidel, Gangolf Hirtz
Date: 2020-11-11 00:00:00
Tasks: Image Generation, Object, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high-resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization, we reach an AP up to 0.84 for class person on High-Definition Analytics dataset.