pwc-archive/papers-with-abstracts huggingface dataset:
Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps
Authors: Kedan Li, Min Jin Chong, Jingen Liu, David Forsyth
Date: 2020-03-22 00:00:00
Tasks: Image Generation, Virtual Try-on

A virtual try-on method takes a product image and an image of a model and produces an image of the model wearing the product. Most methods essentially compute warps from the product image to the model image and combine using image generation methods. However, obtaining a realistic image is challenging because the kinematics of garments is complex and because outline, texture, and shading cues in the image reveal errors to human viewers. The garment must have appropriate drapes; texture must be warped to be consistent with the shape of a draped garment; small details (buttons, collars, lapels, pockets, etc.) must be placed appropriately on the garment, and so on. Evaluation is particularly difficult and is usually qualitative. This paper uses quantitative evaluation on a challenging, novel dataset to demonstrate that (a) for any warping method, one can choose target models automatically to improve results, and (b) learning multiple coordinated specialized warpers offers further improvements on results. Target models are chosen by a learned embedding procedure that predicts a representation of the products the model is wearing. This prediction is used to match products to models. Specialized warpers are trained by a method that encourages a second warper to perform well in locations where the first works poorly. The warps are then combined using a U-Net. Qualitative evaluation confirms that these improvements are wholesale over outline, texture shading, and garment details.
Quantifying the relationship between student enrollment patterns and student performance
Authors: Shahab Boumi, Adan Vela, Jacquelyn Chini
Date: 2020-03-22 00:00:00
Tasks: Scheduling

Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While prior research has established full-time students maintain better outcomes then their part-time counterparts, limited study has examined the impact of enrollment patterns or strategies on academic outcomes. In this paper, we applying a Hidden Markov Model to identify and cluster students' enrollment strategies into three different categorizes: full-time, part-time, and mixed-enrollment strategies. Based the enrollment strategies we investigate and compare the academic performance outcomes of each group, taking into account differences between first-time-in-college students and transfer students. Analysis of data collected from the University of Central Florida from 2008 to 2017 indicates that first-time-in-college students that apply a mixed enrollment strategy are closer in performance to full-time students, as compared to part-time students. More importantly, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Similarly, analysis of transfer students shows that a mixed-enrollment strategy is correlated a similar graduation rates as the full-time enrollment strategy, and more than double the graduation rate associated with part-time enrollment. Such a finding suggests that increased engagement through the occasional full-time enrollment leads to better overall outcomes.
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach
Authors: Omar Bouhamed, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
Date: 2020-03-21 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
Authors: Feiyang Cai, Jiani Li, Xenofon Koutsoukos
Date: 2020-03-21 00:00:00
Tasks: Conformal Prediction, Prediction, regression, Self-Driving Cars

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction. The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS. The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder. The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples. We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle. The simulation results show that the method can effectively detect adversarial examples with a short detection delay.
Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN
Authors: Shervin Minaee, Mehdi Minaei, Amirali Abdolrashidi
Date: 2020-03-21 00:00:00
Tasks: Diversity

Generating realistic palmprint (more generally biometric) images has always been an interesting and, at the same time, challenging problem. Classical statistical models fail to generate realistic-looking palmprint images, as they are not powerful enough to capture the complicated texture representation of palmprint images. In this work, we present a deep learning framework based on generative adversarial networks (GAN), which is able to generate realistic palmprint images. To help the model learn more realistic images, we proposed to add a suitable regularization to the loss function, which imposes the line connectivity of generated palmprint images. This is very desirable for palmprints, as the principal lines in palm are usually connected. We apply this framework to a popular palmprint databases, and generate images which look very realistic, and similar to the samples in this database. Through experimental results, we show that the generated palmprint images look very realistic, have a good diversity, and are able to capture different parts of the prior distribution. We also report the Frechet Inception distance (FID) of the proposed model, and show that our model is able to achieve really good quantitative performance in terms of FID score.
Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
Authors: Du Su, Ali Yekkehkhany, Yi Lu, Wenmiao Lu
Date: 2020-03-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Retrieval, Sentence, Sentence Embedding, Sentence-Embedding

We propose a new application of embedding techniques for problem retrieval in adaptive tutoring. The objective is to retrieve problems whose mathematical concepts are similar. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in innovative ways, while still displaying continuity in their relationships. Second, it is difficult for humans to determine a similarity score that is consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps. Prob2Vec achieves 96.88\% accuracy on a problem similarity test, in contrast to 75\% from directly applying state-of-the-art sentence embedding methods. It is interesting that Prob2Vec is able to distinguish very fine-grained differences among problems, an ability humans need time and effort to acquire. In addition, the sub-problem of concept labeling with imbalanced training data set is interesting in its own right. It is a multi-label problem suffering from dimensionality explosion, which we propose ways to ameliorate. We propose the novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification, using an imbalanced training data set.
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
Authors: Ophir Gozes, Hayit Greenspan
Date: 2020-03-20 00:00:00

In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accomplish the task in a fully supervised setting. To create the training image pairs, we employed simulated X-Ray or Digitally Reconstructed Radiographs (DRR), derived from 664 CT scans belonging to the LIDC-IDRI dataset. Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network. For the reconstruction loss, we utilize two loss functions- L1 Loss and perceptual loss. Once the bone structures are extracted, the original image can be enhanced by fusing the original input x-ray and the synthesized "Bone X-ray". We show that our enhancement technique is applicable to real x-ray data, and display our results on the NIH Chest X-Ray-14 dataset.
A new paradigm for accelerating clinical data science at Stanford Medicine
Authors: Somalee Datta, Jose Posada, Garrick Olson, Wencheng Li, Ciaran O'Reilly, Deepa Balraj, Joseph Mesterhazy, Joseph Pallas, Priyamvada Desai, Nigam Shah
Date: 2020-03-17 00:00:00

Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and researchers have demonstrated the ability to reuse that data and AI approaches to derive novel insights, support patient care, and improve care quality. However, the traditional data warehouse and Honest Broker approaches that are in current use, are not scalable. We are establishing a new secure Big Data platform that aims to reduce time to access and analyze data. In this platform, data is anonymized to preserve patient data privacy and made available preparatory to Institutional Review Board (IRB) submission. Furthermore, the data is standardized such that analysis done at Stanford can be replicated elsewhere using the same analytical code and clinical concepts. Finally, the analytics data warehouse integrates with a secure data science computational facility to support large scale data analytics. The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.
SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework
Authors: Conrad James Foley, Sagar Vaze, Mohamed El Amine Seddiq, Alexey Unagaev, Natalia Efremova
Date: 2020-03-16 00:00:00
Tasks: Earth Observation

Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases the likelihood of extreme weather events and reduces the predictability of weather, and non-optimal soil moistures for crops may become more likely. In this work, we a series of LSTM architectures to analyze measurements of soil moisture and vegetation indiced derived from satellite imagery. The system learns to predict the future values of these measurements. These spatially sparse values and indices are used as input features to an interpolation method that infer spatially dense moisture map for a future time point. This has the potential to provide advance warning for soil moistures that may be inhospitable to crops across an area with limited monitoring capacity.
Learning What to Learn for Video Object Segmentation
Authors: Goutam Bhat, Felix Järemo Lawin, Martin Danelljan, Andreas Robinson, Michael Felsberg, Luc van Gool, Radu Timofte
Date: 2020-03-25 00:00:00
Proceeding: ECCV 2020 8
Tasks: Few-Shot Learning, Object, One-shot visual object segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of how to capture and utilize this limited target information remains a fundamental research question. We address this by introducing an end-to-end trainable VOS architecture that integrates a differentiable few-shot learning module. This internal learner is designed to predict a powerful parametric model of the target by minimizing a segmentation error in the first frame. We further go beyond standard few-shot learning techniques by learning what the few-shot learner should learn. This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach. We perform extensive experiments on multiple benchmarks. Our approach sets a new state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an overall score of 81.5, corresponding to a 2.6% relative improvement over the previous best result.
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
Authors: Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola
Date: 2020-03-25 00:00:00
Proceeding: ECCV 2020 8
Tasks: All, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning

The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost. Few-shot learning is widely used as one of the standard benchmarks in meta-learning. In this work, we show that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, followed by training a linear classifier on top of this representation, outperforms state-of-the-art few-shot learning methods. An additional boost can be achieved through the use of self-distillation. This demonstrates that using a good learned embedding model can be more effective than sophisticated meta-learning algorithms. We believe that our findings motivate a rethinking of few-shot image classification benchmarks and the associated role of meta-learning algorithms. Code is available at: http://github.com/WangYueFt/rfs/.
HP2IFS: Head Pose estimation exploiting Partitioned Iterated Function Systems
Authors: Carmen Bisogni, Michele Nappi, Chiara Pero, Stefano Ricciardi
Date: 2020-03-25 00:00:00
Tasks: BIG-bench Machine Learning, Head Pose Estimation, Pose Estimation

Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches.
Training Binary Neural Networks with Real-to-Binary Convolutions
Authors: Brais Martinez, Jing Yang, Adrian Bulat, Georgios Tzimiropoulos
Date: 2020-03-25 00:00:00
Proceeding: ICLR 2020 1
Tasks: Binarization, Classification with Binary Neural Network

This paper shows how to train binary networks to within a few percent points ($\sim 3-5 \%$) of the full precision counterpart. We first show how to build a strong baseline, which already achieves state-of-the-art accuracy, by combining recently proposed advances and carefully adjusting the optimization procedure. Secondly, we show that by attempting to minimize the discrepancy between the output of the binary and the corresponding real-valued convolution, additional significant accuracy gains can be obtained. We materialize this idea in two complementary ways: (1) with a loss function, during training, by matching the spatial attention maps computed at the output of the binary and real-valued convolutions, and (2) in a data-driven manner, by using the real-valued activations, available during inference prior to the binarization process, for re-scaling the activations right after the binary convolution. Finally, we show that, when putting all of our improvements together, the proposed model beats the current state of the art by more than 5% top-1 accuracy on ImageNet and reduces the gap to its real-valued counterpart to less than 3% and 5% top-1 accuracy on CIFAR-100 and ImageNet respectively when using a ResNet-18 architecture. Code available at https://github.com/brais-martinez/real2binary.
Robustness Analysis of the Data-Selective Volterra NLMS Algorithm
Authors: Javad Sharafi, Abbas Maarefparvar
Date: 2020-03-25 00:00:00
Tasks: parameter estimation

Recently, the data-selective adaptive Volterra filters have been proposed; however, up to now, there are not any theoretical analyses on its behavior rather than numerical simulations. Therefore, in this paper, we analyze the robustness (in the sense of l2-stability) of the data-selective Volterra normalized least-mean-square (DS-VNLMS) algorithm. First, we study the local robustness of this algorithm at any iteration, then we propose a global bound for the error/discrepancy in the coefficient vector. Also, we demonstrate that the DS-VNLMS algorithm improves the parameter estimation for the majority of the iterations that an update is implemented. Moreover, we prove that if the noise bound is known, we can set the DS-VNLMS so that it never degrades the estimate. The simulation results corroborate the validity of the executed analysis and demonstrate that the DS-VNLMS algorithm is robust against noise, no matter how its parameters are adopted.
Improved Techniques for Training Single-Image GANs
Authors: Tobias Hinz, Matthew Fisher, Oliver Wang, Stefan Wermter
Date: 2020-03-25 00:00:00
Tasks: Image Generation, single-image-generation

Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance, as it means that generative models can be used in domains where collecting a large dataset is not feasible. However, training a model capable of generating realistic images from only a single sample is a difficult problem. In this work, we conduct a number of experiments to understand the challenges of training these methods and propose some best practices that we found allowed us to generate improved results over previous work in this space. One key piece is that unlike prior single image generation methods, we concurrently train several stages in a sequential multi-stage manner, allowing us to learn models with fewer stages of increasing image resolution. Compared to a recent state of the art baseline, our model is up to six times faster to train, has fewer parameters, and can better capture the global structure of images.
Visual-Inertial Telepresence for Aerial Manipulation
Authors: Jongseok Lee, Ribin Balachandran, Yuri S. Sarkisov, Marco De Stefano, Andre Coelho, Kashmira Shinde, Min Jun Kim, Rudolph Triebel, Konstantin Kondak
Date: 2020-03-25 00:00:00
Tasks: Object, Object Tracking

This paper presents a novel telepresence system for enhancing aerial manipulation capabilities. It involves not only a haptic device, but also a virtual reality that provides a 3D visual feedback to a remotely-located teleoperator in real-time. We achieve this by utilizing onboard visual and inertial sensors, an object tracking algorithm and a pre-generated object database. As the virtual reality has to closely match the real remote scene, we propose an extension of a marker tracking algorithm with visual-inertial odometry. Both indoor and outdoor experiments show benefits of our proposed system in achieving advanced aerial manipulation tasks, namely grasping, placing, force exertion and peg-in-hole insertion.
Not all domains are equally complex: Adaptive Multi-Domain Learning
Authors: Ali Senhaji, Jenni Raitoharju, Moncef Gabbouj, Alexandros Iosifidis
Date: 2020-03-25 00:00:00
Tasks: All

Deep learning approaches are highly specialized and require training separate models for different tasks. Multi-domain learning looks at ways to learn a multitude of different tasks, each coming from a different domain, at once. The most common approach in multi-domain learning is to form a domain agnostic model, the parameters of which are shared among all domains, and learn a small number of extra domain-specific parameters for each individual new domain. However, different domains come with different levels of difficulty; parameterizing the models of all domains using an augmented version of the domain agnostic model leads to unnecessarily inefficient solutions, especially for easy to solve tasks. We propose an adaptive parameterization approach to deep neural networks for multi-domain learning. The proposed approach performs on par with the original approach while reducing by far the number of parameters, leading to efficient multi-domain learning solutions.
Similarity of Neural Networks with Gradients
Authors: Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas Damianou
Date: 2020-03-25 00:00:00
Tasks: Network Pruning

A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies. We define two key steps when comparing models: firstly, the representation abstracted from the learnt model, where we propose to leverage both feature vectors and gradient ones (which are largely ignored in prior work) into designing the representation of a neural network. Secondly, we define the employed similarity index which gives desired invariance properties, and we facilitate the chosen ones with sketching techniques for comparing various datasets efficiently. Empirically, we show that the proposed approach provides a state-of-the-art method for computing similarity of neural networks that are trained independently on different datasets and the tasks defined by the datasets.