pwc-archive/papers-with-abstracts huggingface dataset:
Image Reconstruction Using Deep Learning
Authors: Po-Yu Liu, Edmund Y. Lam
Date: 2018-09-27 00:00:00
Tasks: Deep Learning, Denoising, Image Denoising, Image Reconstruction, Image Segmentation, Semantic Segmentation

This paper proposes a deep learning architecture that attains statistically
significant improvements over traditional algorithms in Poisson image denoising
espically when the noise is strong. Poisson noise commonly occurs in low-light
and photon- limited settings, where the noise can be most accurately modeled by
the Poission distribution. Poisson noise traditionally prevails only in
specific fields such as astronomical imaging. However, with the booming market
of surveillance cameras, which commonly operate in low-light environments, or
mobile phones, which produce noisy night scene pictures due to lower-grade
sensors, the necessity for an advanced Poisson image denoising algorithm has
increased. Deep learning has achieved amazing breakthroughs in other imaging
problems, such image segmentation and recognition, and this paper proposes a
deep learning denoising network that outperforms traditional algorithms in
Poisson denoising especially when the noise is strong. The architecture
incorporates a hybrid of convolutional and deconvolutional layers along with
symmetric connections. The denoising network achieved statistically significant
0.38dB, 0.68dB, and 1.04dB average PSNR gains over benchmark traditional
algorithms in experiments with image peak values 4, 2, and 1. The denoising
network can also operate with shorter computational time while still
outperforming the benchmark algorithm by tuning the reconstruction stride
sizes.
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance
Authors: Wenxi Liu, Yuanlong Yu, Chun-Yang Zhang, Genggeng Liu, Naixue Xiong
Date: 2018-09-27 00:00:00
Tasks: Active Learning, Attribute, Clustering, Event Detection

In recent years, crowd analysis is important for applications such as smart cities, intelligent transportation system, customer behavior prediction, and visual surveillance. Understanding the characteristics of the individual motion in a crowd can be beneficial for social event detection and abnormal detection, but it has rarely been studied. In this paper, we focus on the extraversion measure of individual motions in crowds based on trajectory data. Extraversion is one of typical personalities that is often observed in human crowd behaviors and it can reflect not only the characteristics of the individual motion, but also the that of the holistic crowd motions. To our best knowledge, this is the first attempt to analyze individual extraversion of crowd motions based on trajectories. To accomplish this, we first present a effective composite motion descriptor, which integrates the basic individual motion information and social metrics, to describe the extraversion of each individual in a crowd. The social metrics consider both the neighboring distribution and their interaction pattern. Since our major goal is to learn a universal scoring function that can measure the degrees of extraversion across varied crowd scenes, we incorporate and adapt the active learning technique to the relative attribute approach. Specifically, we assume the social groups in any crowds contain individuals with the similar degree of extraversion. Based on such assumption, we significantly reduce the computation cost by clustering and ranking the trajectories actively. Finally, we demonstrate the performance of our proposed method by measuring the degree of extraversion for real individual trajectories in crowds and analyzing crowd scenes from a real-world dataset.
Queue-based Resampling for Online Class Imbalance Learning
Authors: Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2018-09-27 00:00:00

Online class imbalance learning constitutes a new problem and an emerging
research topic that focusses on the challenges of online learning under class
imbalance and concept drift. Class imbalance deals with data streams that have
very skewed distributions while concept drift deals with changes in the class
imbalance status. Little work exists that addresses these challenges and in
this paper we introduce queue-based resampling, a novel algorithm that
successfully addresses the co-existence of class imbalance and concept drift.
The central idea of the proposed resampling algorithm is to selectively include
in the training set a subset of the examples that appeared in the past. Results
on two popular benchmark datasets demonstrate the effectiveness of queue-based
resampling over state-of-the-art methods in terms of learning speed and
quality.
An analytic theory of generalization dynamics and transfer learning in deep linear networks
Authors: Andrew K. Lampinen, Surya Ganguli
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-Task Learning, Transfer Learning

Much attention has been devoted recently to the generalization puzzle in deep
learning: large, deep networks can generalize well, but existing theories
bounding generalization error are exceedingly loose, and thus cannot explain
this striking performance. Furthermore, a major hope is that knowledge may
transfer across tasks, so that multi-task learning can improve generalization
on individual tasks. However we lack analytic theories that can quantitatively
predict how the degree of knowledge transfer depends on the relationship
between the tasks. We develop an analytic theory of the nonlinear dynamics of
generalization in deep linear networks, both within and across tasks. In
particular, our theory provides analytic solutions to the training and testing
error of deep networks as a function of training time, number of examples,
network size and initialization, and the task structure and SNR. Our theory
reveals that deep networks progressively learn the most important task
structure first, so that generalization error at the early stopping time
primarily depends on task structure and is independent of network size. This
suggests any tight bound on generalization error must take into account task
structure, and explains observations about real data being learned faster than
random data. Intriguingly our theory also reveals the existence of a learning
algorithm that proveably out-performs neural network training through gradient
descent. Finally, for transfer learning, our theory reveals that knowledge
transfer depends sensitively, but computably, on the SNRs and input feature
alignments of pairs of tasks.
Multi-View Frame Reconstruction with Conditional GAN
Authors: Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury
Date: 2018-09-27 00:00:00
Tasks: Generative Adversarial Network

Multi-view frame reconstruction is an important problem particularly when
multiple frames are missing and past and future frames within the camera are
far apart from the missing ones. Realistic coherent frames can still be
reconstructed using corresponding frames from other overlapping cameras. We
propose an adversarial approach to learn the spatio-temporal representation of
the missing frame using conditional Generative Adversarial Network (cGAN). The
conditional input to each cGAN is the preceding or following frames within the
camera or the corresponding frames in other overlapping cameras, all of which
are merged together using a weighted average. Representations learned from
frames within the camera are given more weight compared to the ones learned
from other cameras when they are close to the missing frames and vice versa.
Experiments on two challenging datasets demonstrate that our framework produces
comparable results with the state-of-the-art reconstruction method in a single
camera and achieves promising performance in multi-camera scenario.
Deep Graph Infomax
Authors: Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Inductive Learning, Node Classification

We present Deep Graph Infomax (DGI), a general approach for learning node
representations within graph-structured data in an unsupervised manner. DGI
relies on maximizing mutual information between patch representations and
corresponding high-level summaries of graphs---both derived using established
graph convolutional network architectures. The learnt patch representations
summarize subgraphs centered around nodes of interest, and can thus be reused
for downstream node-wise learning tasks. In contrast to most prior approaches
to unsupervised learning with GCNs, DGI does not rely on random walk
objectives, and is readily applicable to both transductive and inductive
learning setups. We demonstrate competitive performance on a variety of node
classification benchmarks, which at times even exceeds the performance of
supervised learning.
Multi-task Learning for Financial Forecasting
Authors: Tao Ma
Date: 2018-09-27 00:00:00
Tasks: Multi-Task Learning, Time Series, Time Series Analysis, Time Series Forecasting

Financial forecasting is challenging and attractive in machine learning.
There are many classic solutions, as well as many deep learning based methods,
proposed to deal with it yielding encouraging performance. Stock time series
forecasting is the most representative problem in financial forecasting. Due to
the strong connections among stocks, the information valuable for forecasting
is not only included in individual stocks, but also included in the stocks
related to them. However, most previous works focus on one single stock, which
easily ignore the valuable information in others. To leverage more information,
in this paper, we propose a jointly forecasting approach to process multiple
time series of related stocks simultaneously, using multi-task learning
framework. Compared to the previous works, we use multiple networks to forecast
multiple related stocks, using the shared and private information of them
simultaneously through multi-task learning. Moreover, we propose an attention
method learning an optimized weighted combination of shared and private
information based on the idea of Capital Asset Pricing Model (CAPM) to help
forecast. Experimental results on various data show improved forecasting
performance over baseline methods.
Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft
Authors: Teresa Nicole Brooks
Date: 2018-09-27 00:00:00
Tasks: Time Series, Time Series Analysis

This paper explores using a Long short-term memory (LSTM) based sequence
autoencoder to learn interesting features for detecting surveillance aircraft
using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic
Dependent Surveillance - Broadcast) data to ground receivers. The ability of
LSTM networks to model varying length time series data and remember
dependencies that span across events makes it an ideal candidate for
implementing a sequence autoencoder for ADS-B data because of its possible
variable length time series, irregular sampling and dependencies that span
across events.
Variance reduction properties of the reparameterization trick
Authors: Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson
Date: 2018-09-27 00:00:00
Tasks: Variational Inference

The reparameterization trick is widely used in variational inference as it
yields more accurate estimates of the gradient of the variational objective
than alternative approaches such as the score function method. Although there
is overwhelming empirical evidence in the literature showing its success, there
is relatively little research exploring why the reparameterization trick is so
effective. We explore this under the idealized assumptions that the variational
approximation is a mean-field Gaussian density and that the log of the joint
density of the model parameters and the data is a quadratic function that
depends on the variational mean. From this, we show that the marginal variances
of the reparameterization gradient estimator are smaller than those of the
score function gradient estimator. We apply the result of our idealized
analysis to real-world examples.
Diagnostics in Semantic Segmentation
Authors: Vladimir Nekrasov, Chunhua Shen, Ian Reid
Date: 2018-09-27 00:00:00
Tasks: Image Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Over the past years, computer vision community has contributed to enormous
progress in semantic image segmentation, a per-pixel classification task,
crucial for dense scene understanding and rapidly becoming vital in lots of
real-world applications, including driverless cars and medical imaging. Most
recent models are now reaching previously unthinkable numbers (e.g., 89% mean
iou on PASCAL VOC, 83% on CityScapes), and, while intersection-over-union and a
range of other metrics provide the general picture of model performance, in
this paper we aim to extend them into other meaningful and important for
applications characteristics, answering such questions as 'how accurate the
model segmentation is on small objects in the general scene?', or 'what are the
sources of uncertainty that cause the model to make an erroneous prediction?'.
Besides establishing a methodology that covers the performance of a single
model from different perspectives, we also showcase several extensions that can
be worth pursuing in order to further improve current results in semantic
segmentation.
Boosting Trust Region Policy Optimization by Normalizing Flows Policy
Authors: Yunhao Tang, Shipra Agrawal
Date: 2018-09-27 00:00:00

We propose to improve trust region policy search with normalizing flows
policy. We illustrate that when the trust region is constructed by KL
divergence constraints, normalizing flows policy generates samples far from the
'center' of the previous policy iterate, which potentially enables better
exploration and helps avoid bad local optima. Through extensive comparisons, we
show that the normalizing flows policy significantly improves upon baseline
architectures especially on high-dimensional tasks with complex dynamics.
Iterative Document Representation Learning Towards Summarization with Polishing
Authors: Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan
Date: 2018-09-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Extractive Text Summarization, Representation Learning, Sentence, Text Summarization

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.
Smooth Inter-layer Propagation of Stabilized Neural Networks for Classification
Authors: Jingfeng Zhang, Laura Wynter
Date: 2018-09-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Recent work has studied the reasons for the remarkable performance of deep
neural networks in image classification. We examine batch normalization on the
one hand and the dynamical systems view of residual networks on the other hand.
Our goal is in understanding the notions of stability and smoothness of the
inter-layer propagation of ResNets so as to explain when they contribute to
significantly enhanced performance. We postulate that such stability is of
importance for the trained ResNet to transfer.
Vector Learning for Cross Domain Representations
Authors: Shagan Sah, Chi Zhang, Thang Nguyen, Dheeraj Kumar Peri, Ameya Shringi, Raymond Ptucha
Date: 2018-09-27 00:00:00
Tasks: Decoder, Image Captioning, Image Generation, Sentence, Video Captioning

Recently, generative adversarial networks have gained a lot of popularity for
image generation tasks. However, such models are associated with complex
learning mechanisms and demand very large relevant datasets. This work borrows
concepts from image and video captioning models to form an image generative
framework. The model is trained in a similar fashion as recurrent captioning
model and uses the learned weights for image generation. This is done in an
inverse direction, where the input is a caption and the output is an image. The
vector representation of the sentence and frames are extracted from an
encoder-decoder model which is initially trained on similar sentence and image
pairs. Our model conditions image generation on a natural language caption. We
leverage a sequence-to-sequence model to generate synthetic captions that have
the same meaning for having a robust image generation. One key advantage of our
method is that the traditional image captioning datasets can be used for
synthetic sentence paraphrases. Results indicate that images generated through
multiple captions are better at capturing the semantic meaning of the family of
captions.
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
Authors: Albert Pumarola, Antonio Agudo, Lorenzo Porzi, Alberto Sanfeliu, Vincent Lepetit, Francesc Moreno-Noguer
Date: 2018-09-27 00:00:00
Proceeding: CVPR 2018 6

We propose a method for predicting the 3D shape of a deformable surface from
a single view. By contrast with previous approaches, we do not need a
pre-registered template of the surface, and our method is robust to the lack of
texture and partial occlusions. At the core of our approach is a {\it
geometry-aware} deep architecture that tackles the problem as usually done in
analytic solutions: first perform 2D detection of the mesh and then estimate a
3D shape that is geometrically consistent with the image. We train this
architecture in an end-to-end manner using a large dataset of synthetic
renderings of shapes under different levels of deformation, material
properties, textures and lighting conditions. We evaluate our approach on a
test split of this dataset and available real benchmarks, consistently
improving state-of-the-art solutions with a significantly lower computational
time.
Repair-Based Degrees of Database Inconsistency: Computation and Complexity
Authors: Leopoldo Bertossi
Date: 2018-09-27 00:00:00

We propose a generic numerical measure of the inconsistency of a database
with respect to a set of integrity constraints. It is based on an abstract
repair semantics. In particular, an inconsistency measure associated to
cardinality-repairs is investigated in detail. More specifically, it is shown
that it can be computed via answer-set programs, but sometimes its computation
can be intractable in data complexity. However, polynomial-time deterministic
and randomized approximations are exhibited. The behavior of this measure under
small updates is analyzed, obtaining fixed-parameter tractability results.
Furthermore, alternative inconsistency measures are proposed and discussed.
Adaptive Pruning of Neural Language Models for Mobile Devices
Authors: Raphael Tang, Jimmy Lin
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5

Neural language models (NLMs) exist in an accuracy-efficiency tradeoff space
where better perplexity typically comes at the cost of greater computation
complexity. In a software keyboard application on mobile devices, this
translates into higher power consumption and shorter battery life. This paper
represents the first attempt, to our knowledge, in exploring
accuracy-efficiency tradeoffs for NLMs. Building on quasi-recurrent neural
networks (QRNNs), we apply pruning techniques to provide a "knob" to select
different operating points. In addition, we propose a simple technique to
recover some perplexity using a negligible amount of memory. Our empirical
evaluations consider both perplexity as well as energy consumption on a
Raspberry Pi, where we demonstrate which methods provide the best
perplexity-power consumption operating point. At one operating point, one of
the techniques is able to provide energy savings of 40% over the state of the
art with only a 17% relative increase in perplexity.
Unsupervised Person Image Synthesis in Arbitrary Poses
Authors: Albert Pumarola, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2018-09-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation

We present a novel approach for synthesizing photo-realistic images of people
in arbitrary poses using generative adversarial learning. Given an input image
of a person and a desired pose represented by a 2D skeleton, our model renders
the image of the same person under the new pose, synthesizing novel views of
the parts visible in the input image and hallucinating those that are not seen.
This problem has recently been addressed in a supervised manner, i.e., during
training the ground truth images under the new poses are given to the network.
We go beyond these approaches by proposing a fully unsupervised strategy. We
tackle this challenging scenario by splitting the problem into two principal
subtasks. First, we consider a pose conditioned bidirectional generator that
maps back the initially rendered image to the original pose, hence being
directly comparable to the input image without the need to resort to any
training image. Second, we devise a novel loss function that incorporates
content and style terms, and aims at producing images of high perceptual
quality. Extensive experiments conducted on the DeepFashion dataset demonstrate
that the images rendered by our model are very close in appearance to those
obtained by fully supervised approaches.
Growing and Retaining AI Talent for the United States Government
Authors: Edward Raff
Date: 2018-09-27 00:00:00
Tasks: BIG-bench Machine Learning, Position

Artificial Intelligence and Machine Learning have become transformative to a
number of industries, and as such many industries need for AI talent is
increasing the demand for individuals with these skills. This continues to
exacerbate the difficulty of acquiring and retaining talent for the United
States Federal Government, both for its direct employees as well as the
companies that support it. We take the position that by focusing on growing and
retaining current talent through a number of cultural changes, the government
can work to remediate this problem today.
Semantically Invariant Text-to-Image Generation
Authors: Shagan Sah, Dheeraj Peri, Ameya Shringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Ray Ptucha
Date: 2018-09-27 00:00:00
Tasks: Image Captioning, Image Generation, Text to Image Generation, Text-to-Image Generation

Image captioning has demonstrated models that are capable of generating
plausible text given input images or videos. Further, recent work in image
generation has shown significant improvements in image quality when text is
used as a prior. Our work ties these concepts together by creating an
architecture that can enable bidirectional generation of images and text. We
call this network Multi-Modal Vector Representation (MMVR). Along with MMVR, we
propose two improvements to the text conditioned image generation. Firstly, a
n-gram metric based cost function is introduced that generalizes the caption
with respect to the image. Secondly, multiple semantically similar sentences
are shown to help in generating better images. Qualitative and quantitative
evaluations demonstrate that MMVR improves upon existing text conditioned image
generation results by over 20%, while integrating visual and text modalities.