pwc-archive/papers-with-abstracts huggingface dataset:
Feature Quantization Improves GAN Training
Authors: Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen
Date: 2020-04-05 00:00:00
Proceeding: ICML 2020 1
Tasks: Conditional Image Generation, Face Generation, Image Generation, Image-to-Image Translation, Quantization, Translation, Unsupervised Image-To-Image Translation

The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, due to a fragile balance between the fixed target distribution and the progressively generated distribution. In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space. The quantized values of FQ are constructed as an evolving dictionary, which is consistent with feature statistics of the recent distribution history. Hence, FQ implicitly enables robust feature matching in a compact space. Our method can be easily plugged into existing GAN models, with little computational overhead in training. We apply FQ to 3 representative GAN models on 9 benchmarks: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. Extensive experimental results show that the proposed FQ-GAN can improve the FID scores of baseline methods by a large margin on a variety of tasks, achieving new state-of-the-art performance.
Arbitrary Scale Super-Resolution for Brain MRI Images
Authors: Chuan Tan, Jin Zhu, Pietro Lio'
Date: 2020-04-05 00:00:00
Tasks: Brain Tumor Segmentation, Image Super-Resolution, Meta-Learning, SSIM, Super-Resolution, Tumor Segmentation

Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20$\%$ on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images. Check out the webapp here: https://metasrgan.herokuapp.com/ Check out the github tutorial here: https://github.com/pancakewaffles/metasrgan-tutorial
Information Mining for COVID-19 Research From a Large Volume of Scientific Literature
Authors: Sabber Ahamed, Manar Samad
Date: 2020-04-05 00:00:00
Tasks: Articles

The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak of a novel strain of coronavirus in 180 countries. In a desperate effort to discover new drugs and vaccines for COVID-19, many scientists are working around the clock. Their valuable time and effort may benefit from computer-based mining of a large volume of health science literature that is a treasure trove of information. In this paper, we have developed a graph-based model using abstracts of 10,683 scientific articles to find key information on three topics: transmission, drug types, and genome research related to coronavirus. A subgraph is built for each of the three topics to extract more topic-focused information. Within each subgraph, we use a betweenness centrality measurement to rank order the importance of keywords related to drugs, diseases, pathogens, hosts of pathogens, and biomolecules. The results reveal intriguing information about antiviral drugs (Chloroquine, Amantadine, Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viral pathogens (zika, dengue, malaria, and several viruses in the coronaviridae virus family), and proteins and therapeutic mechanisms (oligonucleotide, interferon, glycoprotein) in connection with the core topic of coronavirus. The categorical summary of these keywords and topics may be a useful reference to expedite and recommend new and alternative directions for COVID-19 research.
A Resource for Studying Chatino Verbal Morphology
Authors: Hilaria Cruz, Gregory Stump, Antonios Anastasopoulos
Date: 2020-04-05 00:00:00
Proceeding: LREC 2020 5
Tasks: Lemmatization, Morphological Analysis, Morphological Inflection

We present the first resource focusing on the verbal inflectional morphology of San Juan Quiahije Chatino, a tonal mesoamerican language spoken in Mexico. We provide a collection of complete inflection tables of 198 lemmata, with morphological tags based on the UniMorph schema. We also provide baseline results on three core NLP tasks: morphological analysis, lemmatization, and morphological inflection.
On Tractable Representations of Binary Neural Networks
Authors: Weijia Shi, Andy Shih, Adnan Darwiche, Arthur Choi
Date: 2020-04-05 00:00:00

We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs). Obtaining this function as an OBDD/SDD facilitates the explanation and formal verification of a neural network's behavior. First, we consider the task of verifying the robustness of a neural network, and show how we can compute the expected robustness of a neural network, given an OBDD/SDD representation of it. Next, we consider a more efficient approach for compiling neural networks, based on a pseudo-polynomial time algorithm for compiling a neuron. We then provide a case study in a handwritten digits dataset, highlighting how two neural networks trained from the same dataset can have very high accuracies, yet have very different levels of robustness. Finally, in experiments, we show that it is feasible to obtain compact representations of neural networks as SDDs.
Machine Translation Pre-training for Data-to-Text Generation -- A Case Study in Czech
Authors: Mihir Kale, Scott Roy
Date: 2020-04-05 00:00:00
Tasks: Data-to-Text Generation, Machine Translation, Text Generation, Translation, Transliteration

While there is a large body of research studying deep learning methods for text generation from structured data, almost all of it focuses purely on English. In this paper, we study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. Since the structured data is generally expressed in English, text generation into other languages involves elements of translation, transliteration and copying - elements already encoded in neural machine translation systems. Moreover, since data-to-text corpora are typically small, this task can benefit greatly from pre-training. Based on our experiments on Czech, a morphologically complex language, we find that pre-training lets us train end-to-end models with significantly improved performance, as judged by automatic metrics and human evaluation. We also show that this approach enjoys several desirable properties, including improved performance in low data scenarios and robustness to unseen slot values.
Any-Shot Sequential Anomaly Detection in Surveillance Videos
Authors: Keval Doshi, Yasin Yilmaz
Date: 2020-04-05 00:00:00
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Decision Making, Transfer Learning

Anomaly detection in surveillance videos has been recently gaining attention. Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data. Also, they lack a concrete approach for continuously updating the trained model once new data is available. Furthermore, online decision making is an important but mostly neglected factor in this domain. Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and any-shot learning, which in turn significantly reduces the training complexity and provides a mechanism that can detect anomalies using only a few labeled nominal examples. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning and the any-shot learning capability of statistical detection methods.
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation
Authors: Sreyashi Nag, Mihir Kale, Varun Lakshminarasimhan, Swapnil Singhavi
Date: 2020-04-05 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Data Augmentation, Machine Translation, Translation

We explore ways of incorporating bilingual dictionaries to enable semi-supervised neural machine translation. Conventional back-translation methods have shown success in leveraging target side monolingual data. However, since the quality of back-translation models is tied to the size of the available parallel corpora, this could adversely impact the synthetically generated sentences in a low resource setting. We propose a simple data augmentation technique to address both this shortcoming. We incorporate widely available bilingual dictionaries that yield word-by-word translations to generate synthetic sentences. This automatically expands the vocabulary of the model while maintaining high quality content. Our method shows an appreciable improvement in performance over strong baselines.
ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition
Authors: Qi Song, Qianyi Jiang, Nan Li, Rui Zhang, Xiaolin Wei
Date: 2020-04-05 00:00:00
Tasks: Scene Text Recognition, valid

In recent years, scene text recognition is always regarded as a sequence-to-sequence problem. Connectionist Temporal Classification (CTC) and Attentional sequence recognition (Attn) are two very prevailing approaches to tackle this problem while they may fail in some scenarios respectively. CTC concentrates more on every individual character but is weak in text semantic dependency modeling. Attn based methods have better context semantic modeling ability while tends to overfit on limited training data. In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition. To overcome the weakness of CTC and Attn, both of them are applied in our method but with different modules in two supervised branches which can make a complementary to each other. Moreover, effective spatial and channel attention mechanisms are introduced to eliminate background noise and extract valid foreground information. Finally, a simple rectified network is implemented to rectify irregular text. The ReADS can be trained end-to-end and only word-level annotations are required. Extensive experiments on various benchmarks verify the effectiveness of ReADS which achieves state-of-the-art performance.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Authors: Yuxuan Song, Qiwei Ye, Minkai Xu, Tie-Yan Liu
Date: 2020-04-05 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability Metric~(Wasserstein GANs). With $f$-divergence as the objective function, the discriminator essentially estimates the density ratio, and the estimated ratio proves useful in further improving the sample quality of the generator. However, how to leverage the information contained in the discriminator of Wasserstein GANs (WGAN) is less explored. In this paper, we introduce the Discriminator Contrastive Divergence, which is well motivated by the property of WGAN's discriminator and the relationship between WGAN and energy-based model. Compared to standard GANs, where the generator is directly utilized to obtain new samples, our method proposes a semi-amortized generation procedure where the samples are produced with the generator's output as an initial state. Then several steps of Langevin dynamics are conducted using the gradient of the discriminator. We demonstrate the benefits of significant improved generation on both synthetic data and several real-world image generation benchmarks.
Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset
Authors: Lawrence O. Hall, Rahul Paul, Dmitry B. Goldgof, Gregory M. Goldgof
Date: 2020-04-05 00:00:00
Tasks: Small Data Image Classification

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).
gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors
Authors: Victor Fragoso, Joseph DeGol, Gang Hua
Date: 2020-04-05 00:00:00
Proceeding: CVPR 2020 6

Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalized-camera-model pose-and-scale estimator that utilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g., gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy.
Privacy Shadow: Measuring Node Predictability and Privacy Over Time
Authors: Ivan Brugere, Tanya Y. Berger-Wolf
Date: 2020-04-04 00:00:00
Tasks: Attribute, Link Prediction

The structure of network data enables simple predictive models to leverage local correlations between nodes to high accuracy on tasks such as attribute and link prediction. While this is useful for building better user models, it introduces the privacy concern that a user's data may be re-inferred from the network structure, after they leave the application. We propose the privacy shadow for measuring how long a user remains predictive from an arbitrary time within the network. Furthermore, we demonstrate that the length of the privacy shadow can be predicted for individual users in three real-world datasets.
Inferring Network Structure From Data
Authors: Ivan Brugere, Tanya Y. Berger-Wolf
Date: 2020-04-04 00:00:00
Tasks: Model Selection

Networks are complex models for underlying data in many application domains. In most instances, raw data is not natively in the form of a network, but derived from sensors, logs, images, or other data. Yet, the impact of the various choices in translating this data to a network have been largely unexamined. In this work, we propose a network model selection methodology that focuses on evaluating a network's utility for varying tasks, together with an efficiency measure which selects the most parsimonious model. We demonstrate that this network definition matters in several ways for modeling the behavior of the underlying system.
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Authors: Sarah Leclerc, Erik Smistad, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Thomas Grenier, Carole Lartizien, Pierre-Marc Jodoin, Lasse Lovstakken, Olivier Bernard
Date: 2020-04-04 00:00:00
Tasks: Cardiac Segmentation, Segmentation

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness. In this work, we introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation while enhancing the estimation of clinical indices and reducing the number of outliers. Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i.e. on average a mean absolute error of 1.5mm and a Hausdorff distance of 5.1mm) with 11% of outliers. Moreover, we demonstrate that our method can closely reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.96 and a mean absolute error of 7.6ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.83 and an absolute mean error of 5.0%, producing scores that are slightly below the intra-observer margin. Based on this observation, areas for improvement are suggested.
ObjectNet Dataset: Reanalysis and Correction
Authors: Ali Borji
Date: 2020-04-04 00:00:00
Tasks: Data Augmentation, Image Classification, Object Recognition

Recently, Barbu et al introduced a dataset called ObjectNet which includes objects in daily life situations. They showed a dramatic performance drop of the state of the art object recognition models on this dataset. Due to the importance and implications of their results regarding generalization ability of deep models, we take a second look at their findings. We highlight a major problem with their work which is applying object recognizers to the scenes containing multiple objects rather than isolated objects. The latter results in around 20-30% performance gain using our code. Compared with the results reported in the ObjectNet paper, we observe that around 10-15 % of the performance loss can be recovered, without any test time data augmentation. In accordance with Barbu et al.'s conclusions, however, we also conclude that deep models suffer drastically on this dataset. Thus, we believe that ObjectNet remains a challenging dataset for testing the generalization power of models beyond datasets on which they have been trained.
FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points
Authors: Ahmed H. Shahin, Prateek Munjal, Ling Shao, Shadab Khan
Date: 2020-04-04 00:00:00
Tasks: Interactive Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation from user inputs has been actively studied to facilitate interactive segmentation for data annotation and other applications. Recent studies have shown that extreme points can be effectively used to encode user inputs. A heat map generated from the extreme points can be appended to the RGB image and input to the model for training. In this study, we present FAIRS -- a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We propose a novel approach for effectively encoding the user input from extreme points and corrective clicks, in a novel and scalable manner that allows the network to work with a variable number of clicks, including corrective clicks for output refinement. We also integrate a dual attention module with our approach to increase the efficacy of the model in preferentially attending to the objects. We demonstrate that these additions help achieve significant improvements over state-of-the-art in dense object segmentation from user inputs, on multiple large-scale datasets. Through experiments, we demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
Using Machine Learning Approach for Computational Substructure in Real-Time Hybrid Simulation
Authors: Elif Ecem Bas, Mohamed A. Moustafa, David Feil-Seifer, Janelle Blankenburg
Date: 2020-04-04 00:00:00
Tasks: BIG-bench Machine Learning

Hybrid simulation (HS) is a widely used structural testing method that combines a computational substructure with a numerical model for well-understood components and an experimental substructure for other parts of the structure that are physically tested. One challenge for fast HS or real-time HS (RTHS) is associated with the analytical substructures of relatively complex structures, which could have large number of degrees of freedoms (DOFs), for instance. These large DOFs computations could be hard to perform in real-time, even with the all current hardware capacities. In this study, a metamodeling technique is proposed to represent the structural dynamic behavior of the analytical substructure. A preliminary study is conducted where a one-bay one-story concentrically braced frame (CBF) is tested under earthquake loading by using a compact HS setup at the University of Nevada, Reno. The experimental setup allows for using a small-scale brace as the experimental substructure combined with a steel frame at the prototype full-scale for the analytical substructure. Two different machine learning algorithms are evaluated to provide a valid and useful metamodeling solution for analytical substructure. The metamodels are trained with the available data that is obtained from the pure analytical solution of the prototype steel frame. The two algorithms used for developing the metamodels are: (1) linear regression (LR) model, and (2) basic recurrent neural network (RNN). The metamodels are first validated against the pure analytical response of the structure. Next, RTHS experiments are conducted by using metamodels. RTHS test results using both LR and RNN models are evaluated, and the advantages and disadvantages of these models are discussed.