pwc-archive/papers-with-abstracts huggingface dataset:
Bilinear Attention Networks
Authors: Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Attention networks in multimodal learning provide an efficient way to utilize
given visual information selectively. However, the computational cost to learn
attention distributions for every pair of multimodal input channels is
prohibitively expensive. To solve this problem, co-attention builds two
separate attention distributions for each modality neglecting the interaction
between multimodal inputs. In this paper, we propose bilinear attention
networks (BAN) that find bilinear attention distributions to utilize given
vision-language information seamlessly. BAN considers bilinear interactions
among two groups of input channels, while low-rank bilinear pooling extracts
the joint representations for each pair of channels. Furthermore, we propose a
variant of multimodal residual networks to exploit eight-attention maps of the
BAN efficiently. We quantitatively and qualitatively evaluate our model on
visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing
that BAN significantly outperforms previous methods and achieves new
state-of-the-arts on both datasets.
Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras
Authors: Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
Date: 2018-05-21 00:00:00
Tasks: Object, object-detection, Object Detection

Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption. Becoming available only very recently, a limited amount of work addresses object detection on these devices. In this paper we propose two neural networks architectures for object detection: YOLE, which integrates the events into surfaces and uses a frame-based model to process them, and fcYOLE, an asynchronous event-based fully convolutional network which uses a novel and general formalization of the convolutional and max pooling layers to exploit the sparsity of camera events. We evaluate the algorithm with different extensions of publicly available datasets and on a novel synthetic dataset.
Learning Tree-based Deep Model for Recommender Systems
Authors: Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, Kun Gai
Date: 2018-01-08 00:00:00
Tasks: Recommendation Systems, Retrieval

Model-based methods for recommender systems have been studied extensively in
recent years. In systems with large corpus, however, the calculation cost for
the learnt model to predict all user-item preferences is tremendous, which
makes full corpus retrieval extremely difficult. To overcome the calculation
barriers, models such as matrix factorization resort to inner product form
(i.e., model user-item preference as the inner product of user, item latent
factors) and indexes to facilitate efficient approximate k-nearest neighbor
searches. However, it still remains challenging to incorporate more expressive
interaction forms between user and item features, e.g., interactions through
deep neural networks, because of the calculation cost.
  In this paper, we focus on the problem of introducing arbitrary advanced
models to recommender systems with large corpus. We propose a novel tree-based
method which can provide logarithmic complexity w.r.t. corpus size even with
more expressive models such as deep neural networks. Our main idea is to
predict user interests from coarse to fine by traversing tree nodes in a
top-down fashion and making decisions for each user-node pair. We also show
that the tree structure can be jointly learnt towards better compatibility with
users' interest distribution and hence facilitate both training and prediction.
Experimental evaluations with two large-scale real-world datasets show that the
proposed method significantly outperforms traditional methods. Online A/B test
results in Taobao display advertising platform also demonstrate the
effectiveness of the proposed method in production environments.
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Authors: Hyeonseob Nam, Hyo-Eun Kim
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Style Transfer

Real-world image recognition is often challenged by the variability of visual
styles including object textures, lighting conditions, filter effects, etc.
Although these variations have been deemed to be implicitly handled by more
training data and deeper networks, recent advances in image style transfer
suggest that it is also possible to explicitly manipulate the style
information. Extending this idea to general visual recognition problems, we
present Batch-Instance Normalization (BIN) to explicitly normalize unnecessary
styles from images. Considering certain style features play an essential role
in discriminative tasks, BIN learns to selectively normalize only disturbing
styles while preserving useful styles. The proposed normalization module is
easily incorporated into existing network architectures such as Residual
Networks, and surprisingly improves the recognition performance in various
scenarios. Furthermore, experiments verify that BIN effectively adapts to
completely different tasks like object classification and style transfer, by
controlling the trade-off between preserving and removing style variations. BIN
can be implemented with only a few lines of code using popular deep learning
frameworks.
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices
Authors: Mahsa Lotfi, Mathukumalli Vidyasagar
Date: 2017-08-11 00:00:00
Tasks: All, Compressive Sensing

In this paper we present a new algorithm for compressive sensing that makes
use of binary measurement matrices and achieves exact recovery of ultra sparse
vectors, in a single pass and without any iterations. Due to its noniterative
nature, our algorithm is hundreds of times faster than $\ell_1$-norm
minimization, and methods based on expander graphs, both of which require
multiple iterations. Our algorithm can accommodate nearly sparse vectors, in
which case it recovers index set of the largest components, and can also
accommodate burst noise measurements. Compared to compressive sensing methods
that are guaranteed to achieve exact recovery of all sparse vectors, our method
requires fewer measurements However, methods that achieve statistical recovery,
that is, recovery of almost all but not all sparse vectors, can require fewer
measurements than our method.
Multi-View Stereo with Asymmetric Checkerboard Propagation and Multi-Hypothesis Joint View Selection
Authors: Qingshan Xu, Wenbing Tao
Date: 2018-05-21 00:00:00

In computer vision domain, how to fast and accurately perform multiview
stereo (MVS) is still a challenging problem. In this paper we present a fast
yet accurate method for 3D dense reconstruction, called AMHMVS, built on the
PatchMatch based stereo algorithm. Different from the regular symmetric
propagation scheme, our approach adopts an asymmetric checkerboard propagation
strategy, which can adaptively make effective hypotheses expand further
according to the confidence of current neighbor hypotheses. In order to
aggregate visual information from multiple images better, we propose the
multi-hypothesis joint view selection for each pixel, which leverages a cost
matrix based on the multiple propagated hypotheses to robustly infer an
appropriate aggregation subset parallel. Combined with the above two steps, our
approach not only has the capacity of massively parallel computation, but also
obtains high accuracy and completeness. Experiments on extensive datasets show
that our method achieves more accurate and robust results, and runs faster than
the competing methods.
Evolution-Guided Policy Gradient in Reinforcement Learning
Authors: Shauharda Khadka, Kagan Tumer
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Evolutionary Algorithms, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) algorithms have been successfully applied
to a range of challenging control tasks. However, these methods typically
suffer from three core difficulties: temporal credit assignment with sparse
rewards, lack of effective exploration, and brittle convergence properties that
are extremely sensitive to hyperparameters. Collectively, these challenges
severely limit the applicability of these approaches to real-world problems.
Evolutionary Algorithms (EAs), a class of black box optimization techniques
inspired by natural evolution, are well suited to address each of these three
challenges. However, EAs typically suffer from high sample complexity and
struggle to solve problems that require optimization of a large number of
parameters. In this paper, we introduce Evolutionary Reinforcement Learning
(ERL), a hybrid algorithm that leverages the population of an EA to provide
diversified data to train an RL agent, and reinserts the RL agent into the EA
population periodically to inject gradient information into the EA. ERL
inherits EA's ability of temporal credit assignment with a fitness metric,
effective exploration with a diverse set of policies, and stability of a
population-based approach and complements it with off-policy DRL's ability to
leverage gradients for higher sample efficiency and faster learning.
Experiments in a range of challenging continuous control benchmarks demonstrate
that ERL significantly outperforms prior DRL and EA methods.
Bayesian posterior approximation via greedy particle optimization
Authors: Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
Date: 2018-05-21 00:00:00
Tasks: Bayesian Inference, Variational Inference

In Bayesian inference, the posterior distributions are difficult to obtain
analytically for complex models such as neural networks. Variational inference
usually uses a parametric distribution for approximation, from which we can
easily draw samples. Recently discrete approximation by particles has attracted
attention because of its high expression ability. An example is Stein
variational gradient descent (SVGD), which iteratively optimizes particles.
Although SVGD has been shown to be computationally efficient empirically, its
theoretical properties have not been clarified yet and no finite sample bound
of the convergence rate is known. Another example is the Stein points (SP)
method, which minimizes kernelized Stein discrepancy directly. Although a
finite sample bound is assured theoretically, SP is computationally inefficient
empirically, especially in high-dimensional problems. In this paper, we propose
a novel method named maximum mean discrepancy minimization by the Frank-Wolfe
algorithm (MMD-FW), which minimizes MMD in a greedy way by the FW algorithm.
Our method is computationally efficient empirically and we show that its finite
sample convergence bound is in a linear order in finite dimensions.
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Authors: Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Date: 2018-05-21 00:00:00
Proceeding: ICML 2018 7
Tasks: Clustering, Image Segmentation, Semantic Segmentation

We provide initial seedings to the Quick Shift clustering algorithm, which
approximate the locally high-density regions of the data. Such seedings act as
more stable and expressive cluster-cores than the singleton modes found by
Quick Shift. We establish statistical consistency guarantees for this
modification. We then show strong clustering performance on real datasets as
well as promising applications to image segmentation.
IoT2Vec: Identification of Similar IoT Devices via Activity Footprints
Authors: Kushal Singla, Joy Bose
Date: 2018-05-21 00:00:00

We consider a smart home or smart office environment with a number of IoT
devices connected and passing data between one another. The footprints of the
data transferred can provide valuable information about the devices, which can
be used to (a) identify the IoT devices and (b) in case of failure, to identify
the correct replacements for these devices. In this paper, we generate the
embeddings for IoT devices in a smart home using Word2Vec, and explore the
possibility of having a similar concept for IoT devices, aka IoT2Vec. These
embeddings can be used in a number of ways, such as to find similar devices in
an IoT device store, or as a signature of each type of IoT device. We show
results of a feasibility study on the CASAS dataset of IoT device activity
logs, using our method to identify the patterns in embeddings of various types
of IoT devices in a household.
Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification
Authors: Maneet Singh, Shruti Nagpal, Richa Singh, Mayank Vatsa
Date: 2018-05-21 00:00:00
Tasks: Classification, Gender Classification, General Classification, Marketing

Gender is one of the most common attributes used to describe an individual.
It is used in multiple domains such as human computer interaction, marketing,
security, and demographic reports. Research has been performed to automate the
task of gender recognition in constrained environment using face images,
however, limited attention has been given to gender classification in
unconstrained scenarios. This work attempts to address the challenging problem
of gender classification in multi-spectral low resolution face images. We
propose a robust Class Representative Autoencoder model, termed as AutoGen for
the same. The proposed model aims to minimize the intra-class variations while
maximizing the inter-class variations for the learned feature representations.
Results on visible as well as near infrared spectrum data for different
resolutions and multiple databases depict the efficacy of the proposed model.
Comparative results with existing approaches and two commercial off-the-shelf
systems further motivate the use of class representative features for
classification.
Unsupervised Deep Context Prediction for Background Foreground Separation
Authors: Maryam Sultana, Arif Mahmood, Sajid Javed, Soon Ki Jung
Date: 2018-05-21 00:00:00
Tasks: Image Inpainting, object-detection, Object Detection, Prediction

In many advanced video based applications background modeling is a
pre-processing step to eliminate redundant data, for instance in tracking or
video surveillance applications. Over the past years background subtraction is
usually based on low level or hand-crafted features such as raw color
components, gradients, or local binary patterns. The background subtraction
algorithms performance suffer in the presence of various challenges such as
dynamic backgrounds, photometric variations, camera jitters, and shadows. To
handle these challenges for the purpose of accurate background modeling we
propose a unified framework based on the algorithm of image inpainting. It is
an unsupervised visual feature learning hybrid Generative Adversarial algorithm
based on context prediction. We have also presented the solution of random
region inpainting by the fusion of center region inpaiting and random region
inpainting with the help of poisson blending technique. Furthermore we also
evaluated foreground object detection with the fusion of our proposed method
and morphological operations. The comparison of our proposed method with 12
state-of-the-art methods shows its stability in the application of background
estimation and foreground detection.
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
Authors: S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian Reid
Date: 2018-05-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: object-detection, Object Detection

Many real-world problems, e.g. object detection, have outputs that are
naturally expressed as sets of entities. This creates a challenge for
traditional deep neural networks which naturally deal with structured outputs
such as vectors, matrices or tensors. We present a novel approach for learning
to predict sets with unknown permutation and cardinality using deep neural
networks. Specifically, in our formulation we incorporate the permutation as
unobservable variable and estimate its distribution during the learning process
using alternating optimization. We demonstrate the validity of this new
formulation on two relevant vision problems: object detection, for which our
formulation outperforms state-of-the-art detectors such as Faster R-CNN and
YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set
based network acquired the ability of mimicking arithmetics without any rules
being coded.
Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
Authors: Dwarikanath Mahapatra
Date: 2016-12-07 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation requires consensus ground truth segmentations to
be derived from multiple expert annotations. A novel approach is proposed that
obtains consensus segmentations from experts using graph cuts (GC) and semi
supervised learning (SSL). Popular approaches use iterative Expectation
Maximization (EM) to estimate the final annotation and quantify annotator's
performance. Such techniques pose the risk of getting trapped in local minima.
We propose a self consistency (SC) score to quantify annotator consistency
using low level image features. SSL is used to predict missing annotations by
considering global features and local image consistency. The SC score also
serves as the penalty cost in a second order Markov random field (MRF) cost
function optimized using graph cuts to derive the final consensus label. Graph
cut obtains a global maximum without an iterative procedure. Experimental
results on synthetic images, real data of Crohn's disease patients and retinal
images show our final segmentation to be accurate and more consistent than
competing methods.
Predicting Electricity Outages Caused by Convective Storms
Authors: Roope Tervo, Joonas Karjalainen, Alexander Jung
Date: 2018-05-21 00:00:00
Tasks: General Classification

We consider the problem of predicting power outages in an electrical power
grid due to hazards produced by convective storms. These storms produce extreme
weather phenomena such as intense wind, tornadoes and lightning over a small
area. In this paper, we discuss the application of state-of-the-art machine
learning techniques, such as random forest classifiers and deep neural
networks, to predict the amount of damage caused by storms. We cast this
application as a classification problem where the goal is to classify storm
cells into a finite number of classes, each corresponding to a certain amount
of expected damage. The classification method use as input features estimates
for storm cell location and movement which has to be extracted from the raw
data.
  A main challenge of this application is that the training data is heavily
imbalanced as the occurrence of extreme weather events is rare. In order to
address this issue, we applied SMOTE technique.
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
Authors: Wei Wen, Yandan Wang, Feng Yan, Cong Xu, Chunpeng Wu, Yiran Chen, Hai Li
Date: 2018-05-21 00:00:00
Tasks: Deep Learning, Open-Ended Question Answering

In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a
training method because of its efficiency; however, recently, a problem in SGD
gains research interest: sharp minima in Deep Neural Networks (DNNs) have poor
generalization; especially, large-batch SGD tends to converge to sharp minima.
It becomes an open question whether escaping sharp minima can improve the
generalization. To answer this question, we propose SmoothOut framework to
smooth out sharp minima in DNNs and thereby improve generalization. In a
nutshell, SmoothOut perturbs multiple copies of the DNN by noise injection and
averages these copies. Injecting noises to SGD is widely used in the
literature, but SmoothOut differs in lots of ways: (1) a de-noising process is
applied before parameter updating; (2) noise strength is adapted to filter
norm; (3) an alternative interpretation on the advantage of noise injection,
from the perspective of sharpness and generalization; (4) usage of uniform
noise instead of Gaussian noise. We prove that SmoothOut can eliminate sharp
minima. Training multiple DNN copies is inefficient, we further propose an
unbiased stochastic SmoothOut which only introduces the overhead of noise
injecting and de-noising per batch. An adaptive variant of SmoothOut,
AdaSmoothOut, is also proposed to improve generalization. In a variety of
experiments, SmoothOut and AdaSmoothOut consistently improve generalization in
both small-batch and large-batch training on the top of state-of-the-art
solutions.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Authors: Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Two-sample testing

Adversarial perturbations of normal images are usually imperceptible to
humans, but they can seriously confuse state-of-the-art machine learning
models. What makes them so special in the eyes of image classifiers? In this
paper, we show empirically that adversarial examples mainly lie in the low
probability regions of the training distribution, regardless of attack types
and targeted models. Using statistical hypothesis testing, we find that modern
neural density models are surprisingly good at detecting imperceptible image
perturbations. Based on this discovery, we devised PixelDefend, a new approach
that purifies a maliciously perturbed image by moving it back towards the
distribution seen in the training data. The purified image is then run through
an unmodified classifier, making our method agnostic to both the classifier and
the attacking method. As a result, PixelDefend can be used to protect already
deployed models and be combined with other model-specific defenses. Experiments
show that our method greatly improves resilience across a wide variety of
state-of-the-art attacking methods, increasing accuracy on the strongest attack
from 63% to 84% for Fashion MNIST and from 32% to 70% for CIFAR-10.
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
Authors: Dwarikanath Mahapatra, Behzad Bozorgtabar
Date: 2017-10-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

We propose an image super resolution(ISR) method using generative adversarial
networks (GANs) that takes a low resolution input fundus image and generates a
high resolution super resolved (SR) image upto scaling factor of $16$. This
facilitates more accurate automated image analysis, especially for small or
blurred landmarks and pathologies. Local saliency maps, which define each
pixel's importance, are used to define a novel saliency loss in the GAN cost
function. Experimental results show the resulting SR images have perceptual
quality very close to the original images and perform better than competing
methods that do not weigh pixels according to their importance. When used for
retinal vasculature segmentation, our SR images result in accuracy levels close
to those obtained when using the original images.