pwc-archive/papers-with-abstracts huggingface dataset:
Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
Authors: Shehryar Khattak, Christos Papachristos, Kostas Alexis
Date: 2019-03-05 00:00:00

This paper proposes a method for tight fusion of visual, depth and inertial
data in order to extend robotic capabilities for navigation in GPS-denied,
poorly illuminated, and texture-less environments. Visual and depth information
are fused at the feature detection and descriptor extraction levels to augment
one sensing modality with the other. These multimodal features are then further
integrated with inertial sensor cues using an extended Kalman filter to
estimate the robot pose, sensor bias terms, and landmark positions
simultaneously as part of the filter state. As demonstrated through a set of
hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown
to perform reliably in challenging visually-degraded environments using RGB-D
information from a lightweight and low-cost sensor and data from an IMU.
Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
Authors: Shehryar Khattak, Christos Papachristos, Kostas Alexis
Date: 2019-03-05 00:00:00
Tasks: Disaster Response, Navigate, Robot Navigation, Sensor Fusion

With an ever-widening domain of aerial robotic applications, including many
mission critical tasks such as disaster response operations, search and rescue
missions and infrastructure inspections taking place in GPS-denied
environments, the need for reliable autonomous operation of aerial robots has
become crucial. Operating in GPS-denied areas aerial robots rely on a multitude
of sensors to localize and navigate. Visible spectrum cameras are the most
commonly used sensors due to their low cost and weight. However, in
environments that are visually-degraded such as in conditions of poor
illumination, low texture, or presence of obscurants including fog, smoke and
dust, the reliability of visible light cameras deteriorates significantly.
Nevertheless, maintaining reliable robot navigation in such conditions is
essential. In contrast to visible light cameras, thermal cameras offer
visibility in the infrared spectrum and can be used in a complementary manner
with visible spectrum cameras for robot localization and navigation tasks,
without paying the significant weight and power penalty typically associated
with carrying other sensors. Exploiting this fact, in this work we present a
multi-sensor fusion algorithm for reliable odometry estimation in GPS-denied
and degraded visual environments. The proposed method utilizes information from
both the visible and thermal spectra for landmark selection and prioritizes
feature extraction from informative image regions based on a metric over
spatial entropy. Furthermore, inertial sensing cues are integrated to improve
the robustness of the odometry estimation process. To verify our solution, a
set of challenging experiments were conducted inside a) an obscurant filed
machine shop-like industrial environment, as well as b) a dark subterranean
mine in the presence of heavy airborne dust.
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
Authors: Tianyi Li, Mai Xu, Ren Yang, Xiaoming Tao
Date: 2019-03-05 00:00:00
Tasks: Computational Efficiency, Video Compression

High efficiency video coding (HEVC) has brought outperforming efficiency for
video compression. To reduce the compression artifacts of HEVC, we propose a
DenseNet based approach as the in-loop filter of HEVC, which leverages multiple
adjacent frames to enhance the quality of each encoded frame. Specifically, the
higher-quality frames are found by a reference frame selector (RFS). Then, a
deep neural network for multi-frame in-loop filter (named MIF-Net) is developed
to enhance the quality of each encoded frame by utilizing the spatial
information of this frame and the temporal information of its neighboring
higher-quality frames. The MIF-Net is built on the recently developed DenseNet,
benefiting from the improved generalization capacity and computational
efficiency. Finally, experimental results verify the effectiveness of our
multi-frame in-loop filter, outperforming the HM baseline and other
state-of-the-art approaches.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
Authors: Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
Date: 2018-06-23 00:00:00
Tasks: Comment Generation, Multi-Task Learning, Sentence

Most previous work on outfit recommendation focuses on designing visual
features to enhance recommendations. Existing work neglects user comments of
fashion items, which have been proved to be effective in generating
explanations along with better recommendation results. We propose a novel
neural network framework, neural outfit recommendation (NOR), that
simultaneously provides outfit recommendations and generates abstractive
comments. NOR consists of two parts: outfit matching and comment generation.
For outfit matching, we propose a convolutional neural network with a mutual
attention mechanism to extract visual features. The visual features are then
decoded into a rating score for the matching prediction. For abstractive
comment generation, we propose a gated recurrent neural network with a
cross-modality attention mechanism to transform visual features into a concise
sentence. The two parts are jointly trained based on a multi-task learning
framework in an end-to-end back-propagation paradigm. Extensive experiments
conducted on an existing dataset and a collected real-world dataset show NOR
achieves significant improvements over state-of-the-art baselines for outfit
recommendation. Meanwhile, our generated comments achieve impressive ROUGE and
BLEU scores in comparison to human-written comments. The generated comments can
be regarded as explanations for the recommendation results. We release the
dataset and code to facilitate future research.
Streaming Batch Eigenupdates for Hardware Neuromorphic Networks
Authors: Brian D. Hoskins, Matthew W. Daniels, Siyuan Huang, Advait Madhavan, Gina C. Adam, Nikolai Zhitenev, Jabez J. McClelland, Mark D. Stiles
Date: 2019-03-05 00:00:00

Neuromorphic networks based on nanodevices, such as metal oxide memristors,
phase change memories, and flash memory cells, have generated considerable
interest for their increased energy efficiency and density in comparison to
graphics processing units (GPUs) and central processing units (CPUs). Though
immense acceleration of the training process can be achieved by leveraging the
fact that the time complexity of training does not scale with the network size,
it is limited by the space complexity of stochastic gradient descent, which
grows quadratically. The main objective of this work is to reduce this space
complexity by using low-rank approximations of stochastic gradient descent.
This low spatial complexity combined with streaming methods allows for
significant reductions in memory and compute overhead, opening the doors for
improvements in area, time and energy efficiency of training. We refer to this
algorithm and architecture to implement it as the streaming batch eigenupdate
(SBE) approach.
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
Authors: Pasha Khosravi, Yitao Liang, YooJung Choi, Guy Van Den Broeck
Date: 2019-03-05 00:00:00
Tasks: Imputation, Missing Values, Prediction, regression

While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expected under certain ways of substituting the missing values, since they inherently make assumptions about the data distribution they were trained on. In this paper, we propose a novel framework that classifies examples with missing features by computing the expected prediction with respect to a feature distribution. Moreover, we use geometric programming to learn a naive Bayes distribution that embeds a given logistic regression classifier and can efficiently take its expected predictions. Empirical evaluations show that our model achieves the same performance as the logistic regression with all features observed, and outperforms standard imputation techniques when features go missing during prediction time. Furthermore, we demonstrate that our method can be used to generate "sufficient explanations" of logistic regression classifications, by removing features that do not affect the classification.
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search
Authors: Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6

A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image manifold in the sense that there exists no physical process that could have produced the adversarial image. This hypothesis suggests that a successful defense mechanism against adversarial images should aim to project the images back onto the image manifold. We study such defense mechanisms, which approximate the projection onto the unknown image manifold by a nearest-neighbor search against a web-scale image database containing tens of billions of images. Empirical evaluations of this defense strategy on ImageNet suggest that it is very effective in attack settings in which the adversary does not have access to the image database. We also propose two novel attack methods to break nearest-neighbor defenses, and demonstrate conditions under which nearest-neighbor defense fails. We perform a series of ablation experiments, which suggest that there is a trade-off between robustness and accuracy in our defenses, that a large image database (with hundreds of millions of images) is crucial to get good performance, and that careful construction the image database is important to be robust against attacks tailored to circumvent our defenses.
Stabilizing the Lottery Ticket Hypothesis
Authors: Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Date: 2019-03-05 00:00:00

Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have explored the possibility of pruning at initialization time to provide similar benefits during training. In particular, the "lottery ticket hypothesis" conjectures that typical neural networks contain small subnetworks that can train to similar accuracy in a commensurate number of steps. The evidence for this claim is that a procedure based on iterative magnitude pruning (IMP) reliably finds such subnetworks retroactively on small vision tasks. However, IMP fails on deeper networks, and proposed methods to prune before training or train pruned networks encounter similar scaling limitations. In this paper, we argue that these efforts have struggled on deeper networks because they have focused on pruning precisely at initialization. We modify IMP to search for subnetworks that could have been obtained by pruning early in training (0.1% to 7% through) rather than at iteration 0. With this change, it finds small subnetworks of deeper networks (e.g., 80% sparsity on Resnet-50) that can complete the training process to match the accuracy of the original network on more challenging tasks (e.g., ImageNet). In situations where IMP fails at iteration 0, the accuracy benefits of delaying pruning accrue rapidly over the earliest iterations of training. To explain these behaviors, we study subnetwork "stability," finding that - as accuracy improves in this fashion - IMP subnetworks train to parameters closer to those of the full network and do so with improved consistency in the face of gradient noise. These results offer new insights into the opportunity to prune large-scale networks early in training and the behaviors underlying the lottery ticket hypothesis
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Authors: Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu
Date: 2019-03-05 00:00:00
Tasks: Adversarial Attack, Adversarial Defense

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Authors: Belinda Tzen, Maxim Raginsky
Date: 2019-03-05 00:00:00
Tasks: Variational Inference

We introduce and study a class of probabilistic generative models, where the latent object is a finite-dimensional diffusion process on a finite time interval and the observed variable is drawn conditionally on the terminal point of the diffusion. We make the following contributions: We provide a unified viewpoint on both sampling and variational inference in such generative models through the lens of stochastic control. We quantify the expressiveness of diffusion-based generative models. Specifically, we show that one can efficiently sample from a wide class of terminal target distributions by choosing the drift of the latent diffusion from the class of multilayer feedforward neural nets, with the accuracy of sampling measured by the Kullback-Leibler divergence to the target distribution. Finally, we present and analyze a scheme for unbiased simulation of generative models with latent diffusions and provide bounds on the variance of the resulting estimators. This scheme can be implemented as a deep generative model with a random number of layers.
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
Authors: Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra
Date: 2019-03-05 00:00:00
Tasks: Imitation Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid, Variational Inference

In model-based reinforcement learning, the agent interleaves between model
learning and planning. These two components are inextricably intertwined. If
the model is not able to provide sensible long-term prediction, the executed
planner would exploit model flaws, which can yield catastrophic failures. This
paper focuses on building a model that reasons about the long-term future and
demonstrates how to use this for efficient planning and exploration. To this
end, we build a latent-variable autoregressive model by leveraging recent ideas
in variational inference. We argue that forcing latent variables to carry
future information through an auxiliary task substantially improves long-term
predictions. Moreover, by planning in the latent space, the planner's solution
is ensured to be within regions where the model is valid. An exploration
strategy can be devised by searching for unlikely trajectories under the model.
Our method achieves higher reward faster compared to baselines on a variety of
tasks and environments in both the imitation learning and model-based
reinforcement learning settings.
Unsupervised Domain-Specific Deblurring via Disentangled Representations
Authors: Boyu Lu, Jun-Cheng Chen, Rama Chellappa
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Disentanglement, Image Deblurring, Single Image Deblurring

Image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on deblurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text deblurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.
On measuring the iconicity of a face
Authors: Prithviraj Dhar, Carlos D. Castillo, Rama Chellappa
Date: 2019-03-04 00:00:00
Tasks: Face Verification

For a given identity in a face dataset, there are certain iconic images which
are more representative of the subject than others. In this paper, we explore
the problem of computing the iconicity of a face. The premise of the proposed
approach is as follows: For an identity containing a mixture of iconic and non
iconic images, if a given face cannot be successfully matched with any other
face of the same identity, then the iconicity of the face image is low. Using
this information, we train a Siamese Multi-Layer Perceptron network, such that
each of its twins predict iconicity scores of the image feature pair, fed in as
input. We observe the variation of the obtained scores with respect to
covariates such as blur, yaw, pitch, roll and occlusion to demonstrate that
they effectively predict the quality of the image and compare it with other
existing metrics. Furthermore, we use these scores to weight features for
template-based face verification and compare it with media averaging of
features.
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Authors: Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames
Date: 2019-03-04 00:00:00

Many modern nonlinear control methods aim to endow systems with guaranteed
properties, such as stability or safety, and have been successfully applied to
the domain of robotics. However, model uncertainty remains a persistent
challenge, weakening theoretical guarantees and causing implementation failures
on physical systems. This paper develops a machine learning framework centered
around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and
unmodeled dynamics in general robotic systems. Our proposed method proceeds by
iteratively updating estimates of Lyapunov function derivatives and improving
controllers, ultimately yielding a stabilizing quadratic program model-based
controller. We validate our approach on a planar Segway simulation,
demonstrating substantial performance improvements by iteratively refining on a
base model-free controller.
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures
Authors: Hossein Nourkhiz Mahjoub, Behrad Toghi, S M Osman Gani, Yaser P. Fallah
Date: 2019-03-04 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Model Selection

Scalable communication is of utmost importance for reliable dissemination of
time-sensitive information in cooperative vehicular ad-hoc networks (VANETs),
which is, in turn, an essential prerequisite for the proper operation of the
critical cooperative safety applications. The model-based communication (MBC)
is a recently-explored scalability solution proposed in the literature, which
has shown a promising potential to reduce the channel congestion to a great
extent. In this work, based on the MBC notion, a technology-agnostic hybrid
model selection policy for Vehicle-to-Everything (V2X) communication is
proposed which benefits from the characteristics of the non-parametric Bayesian
inference techniques, specifically Gaussian Processes. The results show the
effectiveness of the proposed communication architecture on both reducing the
required message exchange rate and increasing the remote agent tracking
precision.
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes
Authors: Abhishek Patil, Srikanth Malla, Haiming Gang, Yi-Ting Chen
Date: 2019-03-04 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Scene Understanding

3D multi-object detection and tracking are crucial for traffic scene
understanding. However, the community pays less attention to these areas due to
the lack of a standardized benchmark dataset to advance the field. Moreover,
existing datasets (e.g., KITTI) do not provide sufficient data and labels to
tackle challenging scenes where highly interactive and occluded traffic
participants are present. To address the issues, we present the Honda Research
Institute 3D Dataset (H3D), a large-scale full-surround 3D multi-object
detection and tracking dataset collected using a 3D LiDAR scanner. H3D
comprises of 160 crowded and highly interactive traffic scenes with a total of
1 million labeled instances in 27,721 frames. With unique dataset size, rich
annotations, and complex scenes, H3D is gathered to stimulate research on
full-surround 3D multi-object detection and tracking. To effectively and
efficiently annotate a large-scale 3D point cloud dataset, we propose a
labeling methodology to speed up the overall annotation cycle. A standardized
benchmark is created to evaluate full-surround 3D multi-object detection and
tracking algorithms. 3D object detection and tracking algorithms are trained
and tested on H3D. Finally, sources of errors are discussed for the development
of future algorithms.
Model Primitive Hierarchical Lifelong Reinforcement Learning
Authors: Bohan Wu, Jayesh K. Gupta, Mykel J. Kochenderfer
Date: 2019-03-04 00:00:00
Tasks: Hierarchical Reinforcement Learning, Lifelong learning, Meta-Learning, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning interpretable and transferable subpolicies and performing task
decomposition from a single, complex task is difficult. Some traditional
hierarchical reinforcement learning techniques enforce this decomposition in a
top-down manner, while meta-learning techniques require a task distribution at
hand to learn such decompositions. This paper presents a framework for using
diverse suboptimal world models to decompose complex task solutions into
simpler modular subpolicies. This framework performs automatic decomposition of
a single source task in a bottom up manner, concurrently learning the required
modular subpolicies as well as a controller to coordinate them. We perform a
series of experiments on high dimensional continuous action control tasks to
demonstrate the effectiveness of this approach at both complex single task
learning and lifelong learning. Finally, we perform ablation studies to
understand the importance and robustness of different elements in the framework
and limitations to this approach.