pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filtering
Authors: Jessica S. Titensky, Hayden Jananthan, Jeremy Kepner
Date: 2018-09-17 00:00:00

Extended Kalman Filtering (EKF) can be used to propagate and quantify input
uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the
input distribution. This methodology yields results comparable to existing
methods of uncertainty propagation for DNNs while lowering the computational
overhead considerably. Additionally, EKF allows model error to be naturally
incorporated into the output uncertainty.
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
Authors: Dimity Miller, Feras Dayoub, Michael Milford, Niko Sünderhauf
Date: 2018-09-17 00:00:00
Tasks: Clustering, General Classification, object-detection, Object Detection, Semantic Segmentation

There has been a recent emergence of sampling-based techniques for estimating
epistemic uncertainty in deep neural networks. While these methods can be
applied to classification or semantic segmentation tasks by simply averaging
samples, this is not the case for object detection, where detection sample
bounding boxes must be accurately associated and merged. A weak merging
strategy can significantly degrade the performance of the detector and yield an
unreliable uncertainty measure. This paper provides the first in-depth
investigation of the effect of different association and merging strategies. We
compare different combinations of three spatial and two semantic affinity
measures with four clustering methods for MC Dropout with a Single Shot
Multi-Box Detector. Our results show that the correct choice of
affinity-clustering combination can greatly improve the effectiveness of the
classification and spatial uncertainty estimation and the resulting object
detection performance. We base our evaluation on a new mix of datasets that
emulate near open-set conditions (semantically similar unknown classes),
distant open-set conditions (semantically dissimilar unknown classes) and the
common closed-set conditions (only known classes).
Open-world Learning and Application to Product Classification
Authors: Hu Xu, Bing Liu, Lei Shu, P. Yu
Date: 2018-09-17 00:00:00
Tasks: Classification, General Classification, Meta-Learning

Classic supervised learning makes the closed-world assumption, meaning that
classes seen in testing must have been seen in training. However, in the
dynamic world, new or unseen class examples may appear constantly. A model
working in such an environment must be able to reject unseen classes (not seen
or used in training). If enough data is collected for the unseen classes, the
system should incrementally learn to accept/classify them. This learning
paradigm is called open-world learning (OWL). Existing OWL methods all need
some form of re-training to accept or include the new classes in the overall
model. In this paper, we propose a meta-learning approach to the problem. Its
key novelty is that it only needs to train a meta-classifier, which can then
continually accept new classes when they have enough labeled data for the
meta-classifier to use, and also detect/reject future unseen classes. No
re-training of the meta-classifier or a new overall classifier covering all old
and new classes is needed. In testing, the method only uses the examples of the
seen classes (including the newly added classes) on-the-fly for classification
and rejection. Experimental results demonstrate the effectiveness of the new
approach.
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
Authors: Jie Wen, Zheng Zhang, Yong Xu, Zuofeng Zhong
Date: 2018-09-17 00:00:00
Tasks: Clustering, Graph Embedding, Incomplete multi-view clustering, MULTI-VIEW LEARNING

Clustering with incomplete views is a challenge in multi-view clustering. In
this paper, we provide a novel and simple method to address this issue.
Specifically, the proposed method simultaneously exploits the local information
of each view and the complementary information among views to learn the common
latent representation for all samples, which can greatly improve the
compactness and discriminability of the obtained representation. Compared with
the conventional graph embedding methods, the proposed method does not
introduce any extra regularization term and corresponding penalty parameter to
preserve the local structure of data, and thus does not increase the burden of
extra parameter selection. By imposing the orthogonal constraint on the basis
matrix of each view, the proposed method is able to handle the out-of-sample.
Moreover, the proposed method can be viewed as a unified framework for
multi-view learning since it can handle both incomplete and complete multi-view
clustering and classification tasks. Extensive experiments conducted on several
multi-view datasets prove that the proposed method can significantly improve
the clustering performance.
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Authors: Tao Ruan, Ting Liu, Zilong Huang, Yunchao Wei, Shikui Wei, Yao Zhao, Thomas Huang
Date: 2018-09-17 00:00:00
Tasks: Human Parsing, Person Re-Identification, Semantic Segmentation

Human parsing has received considerable interest due to its wide application
potentials. Nevertheless, it is still unclear how to develop an accurate human
parsing system in an efficient and elegant way. In this paper, we identify
several useful properties, including feature resolution, global context
information and edge details, and perform rigorous analyses to reveal how to
leverage them to benefit the human parsing task. The advantages of these useful
properties finally result in a simple yet effective Context Embedding with Edge
Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end
trainable and can be easily adopted for conducting multiple human parsing.
Benefiting the superiority of CE2P, we achieved the 1st places on all three
human parsing benchmarks. Without any bells and whistles, we achieved 56.50\%
(mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP
v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and
1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help
ease future research in single/multiple human parsing. Code has been made
available at \url{https://github.com/liutinglt/CE2P}.
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Authors: Zheng Zhang, Li Liu, Jie Qin, Fan Zhu, Fumin Shen, Yong Xu, Ling Shao, Heng Tao Shen
Date: 2018-09-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Clustering, Image Clustering, Representation Learning

How to economically cluster large-scale multi-view images is a long-standing
problem in computer vision. To tackle this challenge, we introduce a novel
approach named Highly-economized Scalable Image Clustering (HSIC) that
radically surpasses conventional image clustering methods via binary
compression. We intuitively unify the binary representation learning and
efficient binary cluster structure learning into a joint framework. In
particular, common binary representations are learned by exploiting both
sharable and individual information across multiple views to capture their
underlying correlations. Meanwhile, cluster assignment with robust binary
centroids is also performed via effective discrete optimization under L21-norm
constraint. By this means, heavy continuous-valued Euclidean distance
computations can be successfully reduced by efficient binary XOR operations
during the clustering procedure. To our best knowledge, HSIC is the first
binary clustering work specifically designed for scalable multi-view image
clustering. Extensive experimental results on four large-scale image datasets
show that HSIC consistently outperforms the state-of-the-art approaches, whilst
significantly reducing computational time and memory footprint.
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis
Authors: Alexander Wong, Mohammad Javad Shafiee, Brendan Chwyl, Francis Li
Date: 2018-09-17 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

The tremendous potential exhibited by deep learning is often offset by
architectural and computational complexity, making widespread deployment a
challenge for edge scenarios such as mobile and other consumer devices. To
tackle this challenge, we explore the following idea: Can we learn generative
machines to automatically generate deep neural networks with efficient network
architectures? In this study, we introduce the idea of generative synthesis,
which is premised on the intricate interplay between a generator-inquisitor
pair that work in tandem to garner insights and learn to generate highly
efficient deep neural networks that best satisfies operational requirements.
What is most interesting is that, once a generator has been learned through
generative synthesis, it can be used to generate not just one but a large
variety of different, unique highly efficient deep neural networks that satisfy
operational requirements. Experimental results for image classification,
semantic segmentation, and object detection tasks illustrate the efficacy of
generative synthesis in producing generators that automatically generate highly
efficient deep neural networks (which we nickname FermiNets) with higher model
efficiency and lower computational costs (reaching >10x more efficient and
fewer multiply-accumulate operations than several tested state-of-the-art
networks), as well as higher energy efficiency (reaching >4x improvements in
image inferences per joule consumed on a Nvidia Tegra X2 mobile processor). As
such, generative synthesis can be a powerful, generalized approach for
accelerating and improving the building of deep neural networks for on-device
edge scenarios.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Authors: Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan
Date: 2018-09-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Conversational Response Generation, Diversity, Informativeness

Responses generated by neural conversational models tend to lack
informativeness and diversity. We present Adversarial Information Maximization
(AIM), an adversarial learning strategy that addresses these two related but
distinct problems. To foster response diversity, we leverage adversarial
training that allows distributional matching of synthetic and real responses.
To improve informativeness, our framework explicitly optimizes a variational
lower bound on pairwise mutual information between query and response.
Empirical results from automatic and human evaluations demonstrate that our
methods significantly boost informativeness and diversity.
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches
Authors: Yuezun Li, Xiao Bian, Ming-Ching Chang, Siwei Lyu
Date: 2018-09-16 00:00:00
Tasks: Object, Region Proposal

Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object. The SSM is referred to the Region Proposal Network used in a two-stage object detector or the single-stage object detector itself. The SSM is typically a fully convolutional neural network which generates output in a single forward pass. Due to the excessive convolutions used in SSM, the actual receptive field is larger than the object itself. As such, we propose a novel method to corrupt object detectors by generating imperceptible patches only in the background. Our method can find a few background patches for perturbation, which can effectively decrease true positives and dramatically increase false positives. Efficacy is demonstrated on 5 two-stage object detectors and 8 single-stage object detectors on the MS COCO 2014 dataset. Results indicate that perturbations with small distortions outside the bounding box of object region can still severely damage the detection performance.
Latent Space Optimal Transport for Generative Models
Authors: Huidong Liu, Yang Guo, Na lei, Zhixin Shu, Shing-Tung Yau, Dimitris Samaras, Xianfeng GU
Date: 2018-09-16 00:00:00

Variational Auto-Encoders enforce their learned intermediate latent-space
data distribution to be a simple distribution, such as an isotropic Gaussian.
However, this causes the posterior collapse problem and loses manifold
structure which can be important for datasets such as facial images. A GAN can
transform a simple distribution to a latent-space data distribution and thus
preserve the manifold structure, but optimizing a GAN involves solving a
Min-Max optimization problem, which is difficult and not well understood so
far. Therefore, we propose a GAN-like method to transform a simple distribution
to a data distribution in the latent space by solving only a minimization
problem. This minimization problem comes from training a discriminator between
a simple distribution and a latent-space data distribution. Then, we can
explicitly formulate an Optimal Transport (OT) problem that computes the
desired mapping between the two distributions. This means that we can transform
a distribution without solving the difficult Min-Max optimization problem.
Experimental results on an eight-Gaussian dataset show that the proposed OT can
handle multi-cluster distributions. Results on the MNIST and the CelebA
datasets validate the effectiveness of the proposed method.
Robust Adversarial Perturbation on Deep Proposal-based Models
Authors: Yuezun Li, Daniel Tian, Ming-Ching Chang, Xiao Bian, Siwei Lyu
Date: 2018-09-16 00:00:00
Tasks: Instance Segmentation, Region Proposal, Segmentation, Semantic Segmentation

Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterative algorithm. Evaluations are performed on the MS COCO 2014 dataset for the adversarial attacking of 6 state-of-the-art object detectors and 2 instance segmentation algorithms. Experimental results demonstrate the efficacy of the proposed method.
Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path Finding through Combination of Satisfiability Modulo Theories and Conflict-based Search
Authors: Pavel Surynek
Date: 2018-09-16 00:00:00
Tasks: Multi-Agent Path Finding

We address item relocation problems in graphs in this paper. We assume items
placed in vertices of an undirected graph with at most one item per vertex.
Items can be moved across edges while various constraints depending on the type
of relocation problem must be satisfied. We introduce a general problem
formulation that encompasses known types of item relocation problems such as
multi-agent path finding (MAPF) and token swapping (TSWAP). In this formulation
we express two new types of relocation problems derived from token swapping
that we call token rotation (TROT) and token permutation (TPERM). Our solving
approach for item relocation combines satisfiability modulo theory (SMT) with
conflict-based search (CBS). We interpret CBS in the SMT framework where we
start with the basic model and refine the model with a collision resolution
constraint whenever a collision between items occurs in the current solution.
The key difference between the standard CBS and our SMT-based modification of
CBS (SMT-CBS) is that the standard CBS branches the search to resolve the
collision while in SMT-CBS we iteratively add a single disjunctive collision
resolution constraint. Experimental evaluation on several benchmarks shows that
the SMT-CBS algorithm significantly outperforms the standard CBS. We also
compared SMT-CBS with a modification of the SAT-based MDD-SAT solver that uses
an eager modeling of item relocation in which all potential collisions are
eliminated by constrains in advance. Experiments show that lazy approach in
SMT-CBS produce fewer constraint than MDD-SAT and also achieves faster solving
run-times.
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Authors: Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael. I. Jordan, Nir Yosef
Date: 2018-09-16 00:00:00
Tasks: General Classification

Class labels are often imperfectly observed, due to mistakes and to genuine
ambiguity among classes. We propose a new semi-supervised deep generative model
that explicitly models noisy labels, called the Mislabeled VAE (M-VAE). The
M-VAE can perform better than existing deep generative models which do not
account for label noise. Additionally, the derivation of M-VAE gives new
theoretical insights into the popular M1+M2 semi-supervised model.
3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair
Authors: Jian-Qing Zheng, Xiao-Yun Zhou, Celia Riga, Guang-Zhong Yang
Date: 2018-09-16 00:00:00
Tasks: Computed Tomography (CT), Graph Matching, Image to Point Cloud Registration

The current standard of intra-operative navigation during Fenestrated
Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between
inserted devices and aortic branches. The navigation commonly via 2D
fluoroscopic images, lacks anatomical information, resulting in longer
operation hours and radiation exposure. In this paper, a framework for
real-time 3D robotic path planning from a single 2D fluoroscopic image of
Abdominal Aortic Aneurysm (AAA) is introduced. A graph matching method is
proposed to establish the correspondence between the 3D preoperative and 2D
intra-operative AAA skeletons, and then the two skeletons are registered by
skeleton deformation and regularization in respect to skeleton length and
smoothness. Furthermore, deep learning was used to segment 3D pre-operative AAA
from Computed Tomography (CT) scans to facilitate the framework automation.
Simulation, phantom and patient AAA data sets have been used to validate the
proposed framework. 3D distance error of 2mm was achieved in the phantom setup.
Performance advantages were also achieved in terms of accuracy, robustness and
time-efficiency. All the code will be open source.
The Space-Efficient Core of Vadalog
Authors: Gerald Berger, Georg Gottlob, Andreas Pieris, Emanuel Sallinger
Date: 2018-09-16 00:00:00
Tasks: Knowledge Graphs

Vadalog is a system for performing complex reasoning tasks such as those
required in advanced knowledge graphs. The logical core of the underlying
Vadalog language is the warded fragment of tuple-generating dependencies
(TGDs). This formalism ensures tractable reasoning in data complexity, while a
recent analysis focusing on a practical implementation led to the reasoning
algorithm around which the Vadalog system is built. A fundamental question that
has emerged in the context of Vadalog is the following: can we limit the
recursion allowed by wardedness in order to obtain a formalism that provides a
convenient syntax for expressing useful recursive statements, and at the same
time achieves space-efficiency? After analyzing several real-life examples of
warded sets of TGDs provided by our industrial partners, as well as recent
benchmarks, we observed that recursion is often used in a restricted way: the
body of a TGD contains at most one atom whose predicate is mutually recursive
with a predicate in the head. We show that this type of recursion, known as
piece-wise linear in the Datalog literature, is the answer to our main
question. We further show that piece-wise linear recursion alone, without the
wardedness condition, is not enough as it leads to the undecidability of
reasoning. We finally study the relative expressiveness of the query languages
based on (piece-wise linear) warded sets of TGDs.
Maximum-Entropy Fine-Grained Classification
Authors: Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
Date: 2018-09-16 00:00:00
Tasks: Classification, Diversity, Fine-Grained Image Classification, General Classification

Fine-Grained Visual Classification (FGVC) is an important computer vision
problem that involves small diversity within the different classes, and often
requires expert annotators to collect data. Utilizing this notion of small
visual diversity, we revisit Maximum-Entropy learning in the context of
fine-grained classification, and provide a training routine that maximizes the
entropy of the output probability distribution for training convolutional
neural networks on FGVC tasks. We provide a theoretical as well as empirical
justification of our approach, and achieve state-of-the-art performance across
a variety of classification tasks in FGVC, that can potentially be extended to
any fine-tuning task. Our method is robust to different hyperparameter values,
amount of training data and amount of training label noise and can hence be a
valuable tool in many similar problems.
Solving for multi-class: a survey and synthesis
Authors: Peter Mills
Date: 2018-09-16 00:00:00
Tasks: Binary Classification, General Classification, Multi-class Classification, Survey

Many of the best statistical classification algorithms are binary classifiers that can only distinguish between one of two classes. The number of possible ways of generalizing binary classification to multi-class increases exponentially with the number of classes. There is some indication that the best method will depend on the dataset. Hence, we are particularly interested in data-driven solution design, whether based on prior considerations or on empirical examination of the data. Here we demonstrate how a recursive control language can be used to describe a multitude of different partitioning strategies in multi-class classification, including those in most common use. We use it both to manually construct new partitioning configurations as well as to examine those that have been automatically designed. Eight different strategies were tested on eight different datasets using a support vector machine (SVM) as the base binary classifier. Numerical results suggest that a one-size-fits-all solution consisting of one-versus-one is appropriate for most datasets. Three datasets showed better accuracy using different methods. The best solution for the most improved dataset exploited a property of the data to produce an uncertainty coefficient 36\% higher (0.016 absolute gain) than one-vs.-one. For the same dataset, an adaptive solution that empirically examined the data was also more accurate than one-vs.-one while being faster.