pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks
Authors: Elahe Arani, Fahad Sarfraz, Bahram Zonooz
Date: 2020-08-16 00:00:00
Tasks: Adversarial Robustness

Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models. However, there seems to be an inherent trade-off between optimizing the model for accuracy and robustness. To this end, we propose Adversarial Concurrent Training (ACT), which employs adversarial training in a collaborative learning framework whereby we train a robust model in conjunction with a natural model in a minimax game. ACT encourages the two models to align their feature space by using the task-specific decision boundaries and explore the input space more broadly. Furthermore, the natural model acts as a regularizer, enforcing priors on features that the robust model should learn. Our analyses on the behavior of the models show that ACT leads to a robust model with lower model complexity, higher information compression in the learned representations, and high posterior entropy solutions indicative of convergence to a flatter minima. We demonstrate the effectiveness of the proposed approach across different datasets and network architectures. On ImageNet, ACT achieves 68.20% standard accuracy and 44.29% robustness accuracy under a 100-iteration untargeted attack, improving upon the standard adversarial training method's 65.70% standard accuracy and 42.36% robustness.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
Authors: Yanchao Yang, Brian Lai, Stefano Soatto
Date: 2020-08-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: Continual Learning, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation, Unsupervised Object Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the motion field by minimizing the mutual information between segments. Then, it uses the segments to learn object models that can be used for detection in a static image. Static and dynamic models are represented by deep neural networks trained jointly in a bootstrapping strategy, which enables extrapolation to previously unseen objects. While the training process requires motion, the resulting object segmentation network can be used on either static images or videos at inference time. As the volume of seen videos grows, more and more objects are seen moving, priming their detection, which then serves as a regularizer for new objects, turning our method into unsupervised continual learning to segment objects. Our models are compared to the state of the art in both video object segmentation and salient object detection. In the six benchmark datasets tested, our models compare favorably even to those using pixel-level supervision, despite requiring no manual annotation.
Computer-Generated Music for Tabletop Role-Playing Games
Authors: Lucas N. Ferreira, Levi H. S. Lelis, Jim Whitehead
Date: 2020-08-16 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper we present Bardo Composer, a system to generate background music for tabletop role-playing games. Bardo Composer uses a speech recognition system to translate player speech into text, which is classified according to a model of emotion. Bardo Composer then uses Stochastic Bi-Objective Beam Search, a variant of Stochastic Beam Search that we introduce in this paper, with a neural model to generate musical pieces conveying the desired emotion. We performed a user study with 116 participants to evaluate whether people are able to correctly identify the emotion conveyed in the pieces generated by the system. In our study we used pieces generated for Call of the Wild, a Dungeons and Dragons campaign available on YouTube. Our results show that human subjects could correctly identify the emotion of the generated music pieces as accurately as they were able to identify the emotion of pieces written by humans.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline
Authors: Eslam Mohamed, Mahmoud Ewaisha, Mennatullah Siam, Hazem Rashed, Senthil Yogamani, Waleed Hamdy, Muhammad Helmi, Ahmad El-Sallab
Date: 2020-08-16 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, GPU, Instance Segmentation, Motion Segmentation, Multi-Task Learning, object-detection, Object Detection, Segmentation, Semantic Segmentation, Trajectory Planning, Video Object Segmentation, Video Semantic Segmentation

Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues. It enables the detection of unseen objects during training (e.g., moose or a construction truck) based on their motion and independent of their appearance. Although pixel-wise motion segmentation has been studied in autonomous driving literature, it has been rarely addressed at the instance level, which would help separate connected segments of moving objects leading to better trajectory planning. As the main issue is the lack of large public datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples improving upon our KITTIMoSeg dataset. In addition to providing instance level annotations, we have added 4 additional classes which is crucial for studying class agnostic motion segmentation. We adapt YOLACT and implement a motion-based class agnostic instance segmentation model which would act as a baseline for the dataset. We also extend it to an efficient multi-task model which additionally provides semantic instance segmentation sharing the encoder. The model then learns separate prototype coefficients within the class agnostic and semantic heads providing two independent paths of object detection for redundant safety. To obtain real-time performance, we study different efficient encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an improvement of 10% mAP relative to the baseline. Our model improves the previous state of the art motion segmentation method by 3.3%. The dataset and qualitative results video are shared in our website at https://sites.google.com/view/instancemotseg/.
Interpretable Representations in Explainable AI: From Theory to Practice
Authors: Kacper Sokol, Peter Flach
Date: 2020-08-16 00:00:00

Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanatory insights. Notably, the explanation type and its cognitive complexity are directly controlled by the interpretable representation, tweaking which allows to target a particular audience and use case. However, many explainers built upon interpretable representations overlook their merit and fall back on default solutions that often carry implicit assumptions, thereby degrading the explanatory power and reliability of such techniques. To address this problem, we study properties of interpretable representations that encode presence and absence of human-comprehensible concepts. We demonstrate how they are operationalised for tabular, image and text data; discuss their assumptions, strengths and weaknesses; identify their core building blocks; and scrutinise their configuration and parameterisation. In particular, this in-depth analysis allows us to pinpoint their explanatory properties, desiderata and scope for (malicious) manipulation in the context of tabular data where a linear model is used to quantify the influence of interpretable concepts on a black-box prediction. Our findings lead to a range of recommendations for designing trustworthy interpretable representations; specifically, the benefits of class-aware (supervised) discretisation of tabular data, e.g., with decision trees, and sensitivity of image interpretable representations to segmentation granularity and occlusion colour.
Learning Disentangled Expression Representations from Facial Images
Authors: Marah Halawa, Manuel Wöllhaf, Eduardo Vellasques, Urko Sánchez Sanz, Olaf Hellwich
Date: 2020-08-16 00:00:00

Face images are subject to many different factors of variation, especially in unconstrained in-the-wild scenarios. For most tasks involving such images, e.g. expression recognition from video streams, having enough labeled data is prohibitively expensive. One common strategy to tackle such a problem is to learn disentangled representations for the different factors of variation of the observed data using adversarial learning. In this paper, we use a formulation of the adversarial loss to learn disentangled representations for face images. The used model facilitates learning on single-task datasets and improves the state-of-the-art in expression recognition with an accuracy of60.53%on the AffectNetdataset, without using any additional data.
Spontaneous preterm birth prediction using convolutional neural networks
Authors: Tomasz Włodarczyk, Szymon Płotka, Przemysław Rokita, Nicole Sochacki-Wójcicka, Jakub Wójcicki, Michał Lipa, Tomasz Trzciński
Date: 2020-08-16 00:00:00
Tasks: Prediction

An estimated 15 million babies are born too early every year. Approximately 1 million children die each year due to complications of preterm birth (PTB). Many survivors face a lifetime of disability, including learning disabilities and visual and hearing problems. Although manual analysis of ultrasound images (US) is still prevalent, it is prone to errors due to its subjective component and complex variations in the shape and position of organs across patients. In this work, we introduce a conceptually simple convolutional neural network (CNN) trained for segmenting prenatal ultrasound images and classifying task for the purpose of preterm birth detection. Our method efficiently segments different types of cervixes in transvaginal ultrasound images while simultaneously predicting a preterm birth based on extracted image features without human oversight. We employed three popular network models: U-Net, Fully Convolutional Network, and Deeplabv3 for the cervix segmentation task. Based on the conducted results and model efficiency, we decided to extend U-Net by adding a parallel branch for classification task. The proposed model is trained and evaluated on a dataset consisting of 354 2D transvaginal ultrasound images and achieved a segmentation accuracy with a mean Jaccard coefficient index of 0.923 $\pm$ 0.081 and a classification sensitivity of 0.677 $\pm$ 0.042 with a 3.49\% false positive rate. Our method obtained better results in the prediction of preterm birth based on transvaginal ultrasound images compared to state-of-the-art methods.
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Authors: Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
Date: 2020-08-16 00:00:00
Tasks: Efficient Neural Network, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation, Uncertainty Quantification

Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, neural network-based uncertainty quantification methods have been successfully applied to various problems. One of the main limitations of the existing techniques is the high memory requirement during training; which limits their application to processing smaller field-of-views (FOVs) and/or using shallower architectures. In this paper, we investigate the effect of using reversible blocks for building memory-efficient neural network architectures for quantification of segmentation uncertainty. The reversible architecture achieves memory saving by exactly computing the activations from the outputs of the subsequent layers during backpropagation instead of storing the activations for each layer. We incorporate the reversible blocks into a recently proposed architecture called PHiSeg that is developed for uncertainty quantification in medical image segmentation. The reversible architecture, RevPHiSeg, allows training neural networks for quantifying segmentation uncertainty on GPUs with limited memory and processing larger FOVs. We perform experiments on the LIDC-IDRI dataset and an in-house prostate dataset, and present comparisons with PHiSeg. The results demonstrate that RevPHiSeg consumes ~30% less memory compared to PHiSeg while achieving very similar segmentation accuracy.
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Authors: Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Date: 2020-08-16 00:00:00
Tasks: Deep Learning

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identified patients with high CVD mortality risks (AUC of 0.768). We validated our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
Authors: Yaqing Wang, Fenglong Ma, Jing Gao
Date: 2020-08-16 00:00:00
Tasks: Graph Representation Learning, Knowledge Graphs, Representation Learning, Triplet

Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a KG, one possible approach is to map the triplets into vector representations by capturing the semantic meanings of facts. Although many representation learning approaches have been developed for knowledge graphs, these methods are not effective for validation. They usually assume that facts are correct, and thus may overfit noisy facts and fail to detect such facts. Towards effective KG validation, we propose to leverage an external human-curated KG as auxiliary information source to help detect the errors in a target KG. The external KG is built upon human-curated knowledge repositories and tends to have high precision. On the other hand, although the target KG built by information extraction from texts has low precision, it can cover new or domain-specific facts that are not in any human-curated repositories. To tackle this challenging task, we propose a cross-graph representation learning framework, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently. This is achieved by embedding triplets based on their semantic meanings, drawing cross-KG negative samples and estimating a confidence score for each triplet based on its degree of correctness. We evaluate the proposed framework on datasets across different domains. Experimental results show that the proposed framework achieves the best performance compared with the state-of-the-art methods on large-scale KGs.
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are
Authors: Vítor Albiero, Kevin W. Bowyer
Date: 2020-08-16 00:00:00
Tasks: Articles, Face Recognition

Recent news articles have accused face recognition of being "biased", "sexist" or "racist". There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate. However, there is little published research aimed at identifying the cause of lower accuracy for females. For instance, the 2019 Face Recognition Vendor Test that documents lower female accuracy across a broad range of algorithms and datasets also lists "Analyze cause and effect" under the heading "What we did not do". We present the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result. Controlling for equal amount of visible face in the test images reverses the apparent higher false non-match rate for females. Also, principal component analysis indicates that images of two different females are inherently more similar than of two different males, potentially accounting for a difference in false match rates.
False Detection (Positives and Negatives) in Object Detection
Authors: Subrata Goswami
Date: 2020-08-16 00:00:00
Tasks: Object, object-detection, Object Detection, Quantization

Object detection is a very important function of visual perception systems. Since the early days of classical object detection based on HOG to modern deep learning based detectors, object detection has improved in accuracy. Two stage detectors usually have higher accuracy than single stage ones. Both types of detectors use some form of quantization of the search space of rectangular regions of image. There are far more of the quantized elements than true objects. The way these bounding boxes are filtered out possibly results in the false positive and false negatives. This empirical experimental study explores ways of reducing false positives and negatives with labelled data.. In the process also discovered insufficient labelling in Openimage 2019 Object Detection dataset.
A Self-supervised GAN for Unsupervised Few-shot Object Recognition
Authors: Khoi Nguyen, Sinisa Todorovic
Date: 2020-08-16 00:00:00
Tasks: Object, Object Recognition, Self-Supervised Learning, Triplet

This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest. The training and test images do not share object classes. We extend the vanilla GAN with two loss functions, both aimed at self-supervised learning. The first is a reconstruction loss that enforces the discriminator to reconstruct the probabilistically sampled latent code which has been used for generating the "fake" image. The second is a triplet loss that enforces the discriminator to output image encodings that are closer for more similar images. Evaluation, comparisons, and detailed ablation studies are done in the context of few-shot classification. Our approach significantly outperforms the state of the art on the Mini-Imagenet and Tiered-Imagenet datasets.
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Authors: Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
Date: 2020-08-16 00:00:00
Proceeding: ICLR 2021 1
Tasks: Data Augmentation, Multi-Task Learning, Novel View Synthesis, Object

We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. While existing work copes with two or more tasks mainly by multi-task learning of shareable feature representations, we take a different perspective. We focus on the interaction and cooperation between a generative model and a discriminative model, in a way that facilitates knowledge to flow across tasks in complementary directions. To this end, we propose bowtie networks that jointly learn 3D geometric and semantic representations with a feedback loop. Experimental evaluation on challenging fine-grained recognition datasets demonstrates that our synthesized images are realistic from multiple viewpoints and significantly improve recognition performance as ways of data augmentation, especially in the low-data regime. Code and pre-trained models are released at https://github.com/zpbao/bowtie_networks.
Prediction of Homicides in Urban Centers: A Machine Learning Approach
Authors: José Ribeiro, Lair Meneses, Denis Costa, Wando Miranda, Ronnie Alves
Date: 2020-08-16 00:00:00
Tasks: BIG-bench Machine Learning

Relevant research has been highlighted in the computing community to develop machine learning models capable of predicting the occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crime, and analyzing crimes over time. However, models capable of predicting specific crimes, such as homicide, are not commonly found in the current literature. This research presents a machine learning model to predict homicide crimes, using a dataset that uses generic data (without study location dependencies) based on incident report records for 34 different types of crimes, along with time and space data from crime reports. Experimentally, data from the city of Bel\'em - Par\'a, Brazil was used. These data were transformed to make the problem generic, enabling the replication of this model to other locations. In the research, analyses were performed with simple and robust algorithms on the created dataset. With this, statistical tests were performed with 11 different classification methods and the results are related to the prediction's occurrence and non-occurrence of homicide crimes in the month subsequent to the occurrence of other registered crimes, with 76% assertiveness for both classes of the problem, using Random Forest. Results are considered as a baseline for the proposed problem.
Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs
Authors: Changchun Yang, Hengrong Lan, Fei Gao
Date: 2020-08-16 00:00:00

If light passes through the body tissues, focusing only on areas where treatment needs, such as tumors, will revolutionize many biomedical imaging and therapy technologies. So how to focus light through deep inhomogeneous tissues overcoming scattering is Holy Grail in biomedical areas. In this paper, we use deep learning to learn and accelerate the process of phase pre-compensation using wavefront shaping. We present an approach (LoftGAN, light only focuses on treatment needs) for learning the relationship between phase domain X and speckle domain Y . Our goal is not just to learn an inverse mapping F:Y->X such that we can know the corresponding X needed for imaging Y like most work, but also to make focusing that is susceptible to disturbances more robust and precise by ensuring that the phase obtained can be forward mapped back to speckle. So we introduce different constraints to enforce F(Y)=X and H(F(Y))=Y with the transmission mapping H:X->Y. Both simulation and physical experiments are performed to investigate the effects of light focusing to demonstrate the effectiveness of our method and comparative experiments prove the crucial improvement of robustness and precision. Codes are available at https://github.com/ChangchunYang/LoftGAN.