pwc-archive/papers-with-abstracts huggingface dataset:
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
Authors: Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala, Rohit Gajawada, Anoop Namboodiri
Date: 2018-04-09 00:00:00
Tasks: Binarization, Sketch Recognition

Deep neural networks are highly effective at a range of computational tasks.
However, they tend to be computationally expensive, especially in
vision-related problems, and also have large memory requirements. One of the
most effective methods to achieve significant improvements in
computational/spatial efficiency is to binarize the weights and activations in
a network. However, naive binarization results in accuracy drops when applied
to networks for most tasks. In this work, we present a highly generalized,
distribution-aware approach to binarizing deep networks that allows us to
retain the advantages of a binarized network, while reducing accuracy drops. We
also develop efficient implementations for our proposed approach across
different architectures. We present a theoretical analysis of the technique to
show the effective representational power of the resulting layers, and explore
the forms of data they model best. Experiments on popular datasets show that
our technique offers better accuracies than naive binarization, while retaining
the same benefits that binarization provides - with respect to run-time
compression, reduction of computational costs, and power consumption.
First Experiments with a Flexible Infrastructure for Normative Reasoning
Authors: Christoph Benzmüller, Xavier Parent
Date: 2018-04-09 00:00:00
Tasks: Logical Reasoning

A flexible infrastructure for normative reasoning is outlined. A small-scale
demonstrator version of the envisioned system has been implemented in the proof
assistant Isabelle/HOL by utilising the first authors universal logical
reasoning approach based on shallow semantical embeddings in meta-logic HOL.
The need for such a flexible reasoning infrastructure is motivated and
illustrated with a contrary-to-duty example scenario selected from the General
Data Protection Regulation.
PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments
Authors: Ruben Gomez-Ojeda, David Zuñiga-Noël, Francisco-Angel Moreno, Davide Scaramuzza, Javier Gonzalez-Jimenez
Date: 2017-05-26 00:00:00
Tasks: C++ code, Descriptive, Visual Odometry

Traditional approaches to stereo visual SLAM rely on point features to
estimate the camera trajectory and build a map of the environment. In
low-textured environments, though, it is often difficult to find a sufficient
number of reliable point features and, as a consequence, the performance of
such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM
system that combines both points and line segments to work robustly in a wider
variety of scenarios, particularly in those where point features are scarce or
not well-distributed in the image. PL-SLAM leverages both points and segments
at all the instances of the process: visual odometry, keyframe selection,
bundle adjustment, etc. We contribute also with a loop closure procedure
through a novel bag-of-words approach that exploits the combined descriptive
power of the two kinds of features. Additionally, the resulting map is richer
and more diverse in 3D elements, which can be exploited to infer valuable,
high-level scene structures like planes, empty spaces, ground plane, etc. (not
addressed in this work). Our proposal has been tested with several popular
datasets (such as KITTI and EuRoC), and is compared to state of the art methods
like ORB-SLAM, revealing a more robust performance in most of the experiments,
while still running in real-time. An open source version of the PL-SLAM C++
code will be released for the benefit of the community.
Bringing Alive Blurred Moments
Authors: Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Decoder, Image Deblurring, Single Image Deblurring, Video Reconstruction

We present a solution for the goal of extracting a video from a single motion
blurred image to sequentially reconstruct the clear views of a scene as beheld
by the camera during the time of exposure. We first learn motion representation
from sharp videos in an unsupervised manner through training of a convolutional
recurrent video autoencoder network that performs a surrogate task of video
reconstruction. Once trained, it is employed for guided training of a motion
encoder for blurred images. This network extracts embedded motion information
from the blurred image to generate a sharp video in conjunction with the
trained recurrent video decoder. As an intermediate step, we also design an
efficient architecture that enables real-time single image deblurring and
outperforms competing methods across all factors: accuracy, speed, and
compactness. Experiments on real scenes and standard datasets demonstrate the
superiority of our framework over the state-of-the-art and its ability to
generate a plausible sequence of temporally consistent sharp frames.
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
Authors: Ruben Gomez-Ojeda, Zichao Zhang, Javier Gonzalez-Jimenez, Davide Scaramuzza
Date: 2017-07-05 00:00:00
Tasks: Image Enhancement, Visual Odometry

One of the main open challenges in visual odometry (VO) is the robustness to
difficult illumination conditions or high dynamic range (HDR) environments. The
main difficulties in these situations come from both the limitations of the
sensors and the inability to perform a successful tracking of interest points
because of the bold assumptions in VO, such as brightness constancy. We address
this problem from a deep learning perspective, for which we first fine-tune a
Deep Neural Network (DNN) with the purpose of obtaining enhanced
representations of the sequences for VO. Then, we demonstrate how the insertion
of Long Short Term Memory (LSTM) allows us to obtain temporally consistent
sequences, as the estimation depends on previous states. However, the use of
very deep networks does not allow the insertion into a real-time VO framework;
therefore, we also propose a Convolutional Neural Network (CNN) of reduced size
capable of performing faster. Finally, we validate the enhanced representations
by evaluating the sequences produced by the two architectures in several
state-of-art VO algorithms, such as ORB-SLAM and DSO.
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
Authors: Seunghyun Yoon, Joongbo Shin, Kyomin Jung
Date: 2017-10-10 00:00:00
Proceeding: NAACL 2018 6
Tasks: Answer Selection, Clustering, Learning-To-Rank, Question Answering

In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Authors: Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Date: 2018-04-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Decision Making, Reinforcement Learning, Sequential Decision Making

Decentralized (PO)MDPs provide an expressive framework for sequential
decision making in a multiagent system. Given their computational complexity,
recent research has focused on tractable yet practical subclasses of
Dec-POMDPs. We address such a subclass called CDEC-POMDP where the collective
behavior of a population of agents affects the joint-reward and environment
dynamics. Our main contribution is an actor-critic (AC) reinforcement learning
method for optimizing CDEC-POMDP policies. Vanilla AC has slow convergence for
larger problems. To address this, we show how a particular decomposition of the
approximate action-value function over agents leads to effective updates, and
also derive a new way to train the critic based on local reward signals.
Comparisons on a synthetic benchmark and a real-world taxi fleet optimization
problem show that our new AC approach provides better quality solutions than
previous best approaches.
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Authors: Maxim Berman, Amal Rannen Triki, Matthew B. Blaschko
Date: 2017-05-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The Jaccard index, also referred to as the intersection-over-union score, is
commonly employed in the evaluation of image segmentation results given its
perceptual qualities, scale invariance - which lends appropriate relevance to
small objects, and appropriate counting of false negatives, in comparison to
per-pixel losses. We present a method for direct optimization of the mean
intersection-over-union loss in neural networks, in the context of semantic
image segmentation, based on the convex Lov\'asz extension of submodular
losses. The loss is shown to perform better with respect to the Jaccard index
measure than the traditionally used cross-entropy loss. We show quantitative
and qualitative differences between optimizing the Jaccard index per image
versus optimizing the Jaccard index taken over an entire dataset. We evaluate
the impact of our method in a semantic segmentation pipeline and show
substantially improved intersection-over-union segmentation scores on the
Pascal VOC and Cityscapes datasets using state-of-the-art deep learning
segmentation architectures.
Variational 3D-PIV with Sparse Descriptors
Authors: Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler
Date: 2018-04-09 00:00:00

3D Particle Imaging Velocimetry (3D-PIV) aim to recover the flow field in a
volume of fluid, which has been seeded with tracer particles and observed from
multiple camera viewpoints. The first step of 3D-PIV is to reconstruct the 3D
locations of the tracer particles from synchronous views of the volume. We
propose a new method for iterative particle reconstruction (IPR), in which the
locations and intensities of all particles are inferred in one joint energy
minimization. The energy function is designed to penalize deviations between
the reconstructed 3D particles and the image evidence, while at the same time
aiming for a sparse set of particles. We find that the new method, without any
post-processing, achieves significantly cleaner particle volumes than a
conventional, tomographic MART reconstruction, and can handle a wide range of
particle densities. The second step of 3D-PIV is to then recover the dense
motion field from two consecutive particle reconstructions. We propose a
variational model, which makes it possible to directly include physical
properties, such as incompressibility and viscosity, in the estimation of the
motion field. To further exploit the sparse nature of the input data, we
propose a novel, compact descriptor of the local particle layout. Hence, we
avoid the memory-intensive storage of high-resolution intensity volumes. Our
framework is generic and allows for a variety of different data costs
(correlation measures) and regularizers. We quantitatively evaluate it with
both the sum of squared differences (SSD) and the normalized cross-correlation
(NCC), respectively with both a hard and a soft version of the
incompressibility constraint.
Semantic Edge Detection with Diverse Deep Supervision
Authors: Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, Jiawang Bian, DaCheng Tao
Date: 2018-04-09 00:00:00
Tasks: Edge Detection, Object Proposal Generation, Object Recognition, Semantic Segmentation

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. Our motivation comes from the hypothesis that such distinct targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. To this end, we propose a novel fully convolutional neural network using diverse deep supervision (DDS) within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges. To overcome the hypothesized supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated on SBD and Cityscapes datasets.
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
Authors: Isabelle Augenstein, Sebastian Ruder, Anders Søgaard
Date: 2018-02-27 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Multi-Task Learning, Sentiment Analysis

We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
Authors: Bing Zeng, Xinyu Li, Liang Gao, Yuyan Zhang, Haozhen Dong
Date: 2018-04-09 00:00:00

Most real-world optimization problems often come with multiple global optima or local optima. Therefore, increasing niching metaheuristic algorithms, which devote to finding multiple optima in a single run, are developed to solve these multimodal optimization problems. However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently. These two difficulties limited their practicality largely. Based on Whale Swarm Algorithm (WSA) we proposed previously, this paper presents a new multimodal optimizer named WSA with Iterative Counter (WSA-IC) to address these two difficulties. In the one hand, WSA-IC improves the iteration rule of the original WSA for multimodal optimization, which removes the need of specifying different values of attenuation coefficient for different problems to form multiple subpopulations, without introducing any niching parameter. In the other hand, WSA-IC enables the identification of extreme point during iterations relying on two new parameters (i.e., stability threshold Ts and fitness threshold Tf), to jump out of the located extreme point. Moreover, the convergence of WSA-IC is proved. Finally, the proposed WSA-IC is compared with several niching metaheuristic algorithms on CEC2015 niching benchmark test functions and five additional classical multimodal functions with high dimensions. The experimental results demonstrate that WSA-IC statistically outperforms other niching metaheuristic algorithms on most test functions.
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
Authors: Yuchen Zhang, Percy Liang, Moses Charikar
Date: 2017-02-18 00:00:00

We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for
non-convex optimization. The algorithm performs stochastic gradient descent,
where in each step it injects appropriately scaled Gaussian noise to the
update. We analyze the algorithm's hitting time to an arbitrary subset of the
parameter space. Two results follow from our general theory: First, we prove
that for empirical risk minimization, if the empirical risk is point-wise close
to the (smooth) population risk, then the algorithm achieves an approximate
local minimum of the population risk in polynomial time, escaping suboptimal
local minima that only exist in the empirical risk. Second, we show that SGLD
improves on one of the best known learnability results for learning linear
classifiers under the zero-one loss.
A Deep Policy Inference Q-Network for Multi-Agent Systems
Authors: Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Date: 2017-12-21 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We present DPIQN, a deep policy inference Q-network that targets multi-agent
systems composed of controllable agents, collaborators, and opponents that
interact with each other. We focus on one challenging issue in such
systems---modeling agents with varying strategies---and propose to employ
"policy features" learned from raw observations (e.g., raw images) of
collaborators and opponents by inferring their policies. DPIQN incorporates the
learned policy features as a hidden vector into its own deep Q-network (DQN),
such that it is able to predict better Q values for the controllable agents
than the state-of-the-art deep reinforcement learning models. We further
propose an enhanced version of DPIQN, called deep recurrent policy inference
Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN
are trained by an adaptive training procedure, which adjusts the network's
attention to learn the policy features and its own Q-values at different phases
of the training process. We present a comprehensive analysis of DPIQN and
DRPIQN, and highlight their effectiveness and generalizability in various
multi-agent settings. Our models are evaluated in a classic soccer game
involving both competitive and collaborative scenarios. Experimental results
performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate
superior performance to the baseline DQN and deep recurrent Q-network (DRQN)
models. We also explore scenarios in which collaborators or opponents
dynamically change their policies, and show that DPIQN and DRPIQN do lead to
better overall performance in terms of stability and mean scores.
Composing photomosaic images using clustering based evolutionary programming
Authors: Yaodong He, Jianfeng Zhou, Shiu Yin Yuen
Date: 2018-04-09 00:00:00
Tasks: Clustering, Combinatorial Optimization, Evolutionary Algorithms

Photomosaic images are a type of images consisting of various tiny images. A
complete form can be seen clearly by viewing it from a long distance. Small
tiny images which replace blocks of the original image can be seen clearly by
viewing it from a short distance. In the past, many algorithms have been
proposed trying to automatically compose photomosaic images. Most of these
algorithms are designed with greedy algorithms to match the blocks with the
tiny images. To obtain a better visual sense and satisfy some commercial
requirements, a constraint that a tiny image should not be repeatedly used many
times is usually added. With the constraint, the photomosaic problem becomes a
combinatorial optimization problem. Evolutionary algorithms imitating the
process of natural selection are popular and powerful in combinatorial
optimization problems. However, little work has been done on applying
evolutionary algorithms to photomosaic problem. In this paper, we present an
algorithm called clustering based evolutionary programming to deal with the
problem. We give prior knowledge to the optimization algorithm which makes the
optimization process converges faster. In our experiment, the proposed
algorithm is compared with the state of the art algorithms and software. The
results indicate that our algorithm performs the best.
Online Learning with Regularized Kernel for One-class Classification
Authors: Chandan Gautam, Aruna Tiwari, Sundaram Suresh, Kapil Ahuja
Date: 2017-01-17 00:00:00
Tasks: Classification, General Classification, Model Selection, One-Class Classification, One-class classifier

This paper presents an online learning with regularized kernel based
one-class extreme learning machine (ELM) classifier and is referred as online
RK-OC-ELM. The baseline kernel hyperplane model considers whole data in a
single chunk with regularized ELM approach for offline learning in case of
one-class classification (OCC). Further, the basic hyper plane model is adapted
in an online fashion from stream of training samples in this paper. Two
frameworks viz., boundary and reconstruction are presented to detect the target
class in online RKOC-ELM. Boundary framework based one-class classifier
consists of single node output architecture and classifier endeavors to
approximate all data to any real number. However, one-class classifier based on
reconstruction framework is an autoencoder architecture, where output nodes are
identical to input nodes and classifier endeavor to reconstruct input layer at
the output layer. Both these frameworks employ regularized kernel ELM based
online learning and consistency based model selection has been employed to
select learning algorithm parameters. The performance of online RK-OC-ELM has
been evaluated on standard benchmark datasets as well as on artificial datasets
and the results are compared with existing state-of-the art one-class
classifiers. The results indicate that the online learning one-class classifier
is slightly better or same as batch learning based approaches. As, base
classifier used for the proposed classifiers are based on the ELM, hence,
proposed classifiers would also inherit the benefit of the base classifier i.e.
it will perform faster computation compared to traditional autoencoder based
one-class classifier.
A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines
Authors: Shin Kamada, Takumi Ichimura
Date: 2018-04-09 00:00:00

Recently, a high technique of image processing is required to extract the
image features in real time. In our research, the tourist subject data are
collected from the Mobile Phone based Participatory Sensing (MPPS) system. Each
record consists of image files with GPS, geographic location name, user's
numerical evaluation, and comments written in natural language at sightseeing
spots where a user really visits. In our previous research, the famous
landmarks in sightseeing spot can be detected by Clonal Selection Algorithm
with Immunological Memory Cell (CSAIM). However, some landmarks was not
detected correctly by the previous method because they didn't have enough
amount of information for the feature extraction. In order to improve the
weakness, we propose the generation method of immunological memory by
Restricted Boltzmann Machines. To verify the effectiveness of the method, some
experiments for classification of the subjective data are executed by using
machine learning tools for Deep Learning.