pwc-archive/papers-with-abstracts huggingface dataset:
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks
Authors: Zeyuan Chen, Shaoliang Nie, Tianfu Wu, Christopher G. Healey
Date: 2018-01-23 00:00:00
Tasks: Facial Inpainting

We present a deep learning approach for high resolution face completion with
multiple controllable attributes (e.g., male and smiling) under arbitrary
masks. Face completion entails understanding both structural meaningfulness and
appearance consistency locally and globally to fill in "holes" whose content do
not appear elsewhere in an input image. It is a challenging task with the
difficulty level increasing significantly with respect to high resolution, the
complexity of "holes" and the controllable attributes of filled-in fragments.
Our system addresses the challenges by learning a fully end-to-end framework
that trains generative adversarial networks (GANs) progressively from low
resolution to high resolution with conditional vectors encoding controllable
attributes.
  We design novel network architectures to exploit information across multiple
scales effectively and efficiently. We introduce new loss functions encouraging
sharp completion. We show that our system can complete faces with large
structural and appearance variations using a single feed-forward pass of
computation with mean inference time of 0.007 seconds for images at 1024 x 1024
resolution. We also perform a pilot human study that shows our approach
outperforms state-of-the-art face completion methods in terms of rank analysis.
The code will be released upon publication.
Fast Point Spread Function Modeling with Deep Learning
Authors: Jörg Herbel, Tomasz Kacprzak, Adam Amara, Alexandre Refregier, Aurelien Lucchi
Date: 2018-01-23 00:00:00
Tasks: Deep Learning, parameter estimation

Modeling the Point Spread Function (PSF) of wide-field surveys is vital for
many astrophysical applications and cosmological probes including weak
gravitational lensing. The PSF smears the image of any recorded object and
therefore needs to be taken into account when inferring properties of galaxies
from astronomical images. In the case of cosmic shear, the PSF is one of the
dominant sources of systematic errors and must be treated carefully to avoid
biases in cosmological parameters. Recently, forward modeling approaches to
calibrate shear measurements within the Monte-Carlo Control Loops ($MCCL$)
framework have been developed. These methods typically require simulating a
large amount of wide-field images, thus, the simulations need to be very fast
yet have realistic properties in key features such as the PSF pattern. Hence,
such forward modeling approaches require a very flexible PSF model, which is
quick to evaluate and whose parameters can be estimated reliably from survey
data. We present a PSF model that meets these requirements based on a fast
deep-learning method to estimate its free parameters. We demonstrate our
approach on publicly available SDSS data. We extract the most important
features of the SDSS sample via principal component analysis. Next, we
construct our model based on perturbations of a fixed base profile, ensuring
that it captures these features. We then train a Convolutional Neural Network
to estimate the free parameters of the model from noisy images of the PSF. This
allows us to render a model image of each star, which we compare to the SDSS
stars to evaluate the performance of our method. We find that our approach is
able to accurately reproduce the SDSS PSF at the pixel level, which, due to the
speed of both the model evaluation and the parameter estimation, offers good
prospects for incorporating our method into the $MCCL$ framework.
Image Provenance Analysis at Scale
Authors: Daniel Moreira, Aparna Bharati, Joel Brogan, Allan Pinto, Michael Parowski, Kevin W. Bowyer, Patrick J. Flynn, Anderson Rocha, Walter J. Scheirer
Date: 2018-01-19 00:00:00
Tasks: Authorship Verification, Fact Checking

Prior art has shown it is possible to estimate, through image processing and
computer vision techniques, the types and parameters of transformations that
have been applied to the content of individual images to obtain new images.
Given a large corpus of images and a query image, an interesting further step
is to retrieve the set of original images whose content is present in the query
image, as well as the detailed sequences of transformations that yield the
query image given the original images. This is a problem that recently has
received the name of image provenance analysis. In these times of public media
manipulation ( e.g., fake news and meme sharing), obtaining the history of
image transformations is relevant for fact checking and authorship
verification, among many other applications. This article presents an
end-to-end processing pipeline for image provenance analysis, which works at
real-world scale. It employs a cutting-edge image filtering solution that is
custom-tailored for the problem at hand, as well as novel techniques for
obtaining the provenance graph that expresses how the images, as nodes, are
ancestrally connected. A comprehensive set of experiments for each stage of the
pipeline is provided, comparing the proposed solution with state-of-the-art
results, employing previously published datasets. In addition, this work
introduces a new dataset of real-world provenance cases from the social media
site Reddit, along with baseline results.
Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes
Authors: Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker
Date: 2018-01-23 00:00:00
Tasks: Reinforcement Learning

Selection HHs are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechanism is generally used to decide which low-level heuristic should be applied in each decision step. In this paper we analyse whether sophisticated learning mechanisms are always necessary for HHs to perform well. To this end we consider the most simple HHs from the literature and rigorously analyse their performance for the LeadingOnes function. Our analysis shows that the standard Simple Random, Permutation, Greedy and Random Gradient HHs show no signs of learning. While the former HHs do not attempt to learn from the past performance of low-level heuristics, the idea behind the Random Gradient HH is to continue to exploit the currently selected heuristic as long as it is successful. Hence, it is embedded with a reinforcement learning mechanism with the shortest possible memory. However, the probability that a promising heuristic is successful in the next step is relatively low when perturbing a reasonable solution to a combinatorial optimisation problem. We generalise the simple Random Gradient HH so success can be measured over a fixed period of time tau, instead of a single iteration. For LO we prove that the Generalised Random Gradient HH can learn to adapt the neighbourhood size of RLS to optimality during the run. We prove it has the best possible performance achievable with the low-level heuristics. We also prove that the performance of the HH improves as the number of low-level local search heuristics to choose from increases. Finally, we show that the advantages of GRG over RLS and EAs using standard bit mutation increase if the anytime performance is considered. Experimental analyses confirm these results for different problem sizes.
PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation
Authors: Arno Solin, Santiago Cortes, Esa Rahtu, Juho Kannala
Date: 2017-08-02 00:00:00
Tasks: Visual Odometry

This paper presents a novel method for visual-inertial odometry. The method
is based on an information fusion framework employing low-cost IMU sensors and
the monocular camera in a standard smartphone. We formulate a sequential
inference scheme, where the IMU drives the dynamical model and the camera
frames are used in coupling trailing sequences of augmented poses. The novelty
in the model is in taking into account all the cross-terms in the updates, thus
propagating the inter-connected uncertainties throughout the model. Stronger
coupling between the inertial and visual data sources leads to robustness
against occlusion and feature-poor environments. We demonstrate results on data
collected with an iPhone and provide comparisons against the Tango device and
using the EuRoC data set.
Analyzing Language Learned by an Active Question Answering Agent
Authors: Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang
Date: 2018-01-23 00:00:00
Tasks: Information Retrieval, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

We analyze the language learned by an agent trained with reinforcement
learning as a component of the ActiveQA system [Buck et al., 2017]. In
ActiveQA, question answering is framed as a reinforcement learning task in
which an agent sits between the user and a black box question-answering system.
The agent learns to reformulate the user's questions to elicit the optimal
answers. It probes the system with many versions of a question that are
generated via a sequence-to-sequence question reformulation model, then
aggregates the returned evidence to find the best answer. This process is an
instance of \emph{machine-machine} communication. The question reformulation
model must adapt its language to increase the quality of the answers returned,
matching the language of the question answering system. We find that the agent
does not learn transformations that align with semantic intuitions but
discovers through learning classical information retrieval techniques such as
tf-idf re-weighting and stemming.
Experimentally detecting a quantum change point via Bayesian inference
Authors: Shang Yu, Chang-Jiang Huang, Jian-Shun Tang, Zhih-Ahn Jia, Yi-Tao Wang, Zhi-Jin Ke, Wei Liu, Xiao Liu, Zong-Quan Zhou, Ze-Di Cheng, Jin-Shi Xu, Yu-Chun Wu, Yuan-Yuan Zhao, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo, Gael Sentís, Ramon Muñoz-Tapia
Date: 2018-01-23 00:00:00
Tasks: Bayesian Inference, Position

Detecting a change point is a crucial task in statistics that has been
recently extended to the quantum realm. A source state generator that emits a
series of single photons in a default state suffers an alteration at some point
and starts to emit photons in a mutated state. The problem consists in
identifying the point where the change took place. In this work, we consider a
learning agent that applies Bayesian inference on experimental data to solve
this problem. This learning machine adjusts the measurement over each photon
according to the past experimental results finds the change position in an
online fashion. Our results show that the local-detection success probability
can be largely improved by using such a machine learning technique. This
protocol provides a tool for improvement in many applications where a sequence
of identical quantum states is required.
How Generative Adversarial Networks and Their Variants Work: An Overview
Authors: Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon
Date: 2017-11-16 00:00:00
Tasks: Attribute, Domain Adaptation, Image Generation, Translation

Generative Adversarial Networks (GAN) have received wide attention in the
machine learning field for their potential to learn high-dimensional, complex
real data distribution. Specifically, they do not rely on any assumptions about
the distribution and can generate real-like samples from latent space in a
simple manner. This powerful property leads GAN to be applied to various
applications such as image synthesis, image attribute editing, image
translation, domain adaptation and other academic fields. In this paper, we aim
to discuss the details of GAN for those readers who are familiar with, but do
not comprehend GAN deeply or who wish to view GAN from various perspectives. In
addition, we explain how GAN operates and the fundamental meaning of various
objective functions that have been suggested recently. We then focus on how the
GAN can be combined with an autoencoder framework. Finally, we enumerate the
GAN variants that are applied to various tasks and other fields for those who
are interested in exploiting GAN for their research.
Survey on Emotional Body Gesture Recognition
Authors: Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kamińska, Tomasz Sapiński, Sergio Escalera, Gholamreza Anbarjafari
Date: 2018-01-23 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Emotion Recognition, Gesture Recognition, Human Detection, Pose Estimation, Representation Learning, Survey

Automatic emotion recognition has become a trending research topic in the
past decade. While works based on facial expressions or speech abound,
recognizing affect from body gestures remains a less explored topic. We present
a new comprehensive survey hoping to boost research in the field. We first
introduce emotional body gestures as a component of what is commonly known as
"body language" and comment general aspects as gender differences and culture
dependence. We then define a complete framework for automatic emotional body
gesture recognition. We introduce person detection and comment static and
dynamic body pose estimation methods both in RGB and 3D. We then comment the
recent literature related to representation learning and emotion recognition
from images of emotionally expressive gestures. We also discuss multi-modal
approaches that combine speech or face with body gestures for improved emotion
recognition. While pre-processing methodologies (e.g. human detection and pose
estimation) are nowadays mature technologies fully developed for robust large
scale analysis, we show that for emotion recognition the quantity of labelled
data is scarce, there is no agreement on clearly defined output spaces and the
representations are shallow and largely based on naive geometrical
representations.
Boundary Optimizing Network (BON)
Authors: Marco Singh, Akshay Pai
Date: 2018-01-08 00:00:00

Despite all the success that deep neural networks have seen in classifying
certain datasets, the challenge of finding optimal solutions that generalize
still remains. In this paper, we propose the Boundary Optimizing Network (BON),
a new approach to generalization for deep neural networks when used for
supervised learning. Given a classification network, we propose to use a
collaborative generative network that produces new synthetic data points in the
form of perturbations of original data points. In this way, we create a data
support around each original data point which prevents decision boundaries from
passing too close to the original data points, i.e. prevents overfitting. We
show that BON improves convergence on CIFAR-10 using the state-of-the-art
Densenet. We do however observe that the generative network suffers from
catastrophic forgetting during training, and we therefore propose to use a
variation of Memory Aware Synapses to optimize the generative network (called
BON++). On the Iris dataset, we visualize the effect of BON++ when the
generator does not suffer from catastrophic forgetting and conclude that the
approach has the potential to create better boundaries in a higher dimensional
space.
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
Authors: Korsuk Sirinukunwattana, David Snead, David Epstein, Zia Aftab, Imaad Mujeeb, Yee Wah Tsang, Ian Cree, Nasir Rajpoot
Date: 2018-01-23 00:00:00

Distant metastasis is the major cause of death in colorectal cancer (CRC).
Patients at high risk of developing distant metastasis could benefit from
appropriate adjuvant and follow-up treatments if stratified accurately at an
early stage of the disease. Studies have increasingly recognized the role of
diverse cellular components within the tumor microenvironment in the
development and progression of CRC tumors. In this paper, we show that a new
method of automated analysis of digitized images from colorectal cancer tissue
slides can provide important estimates of distant metastasis-free survival
(DMFS, the time before metastasis is first observed) on the basis of details of
the microenvironment. Specifically, we determine what cell types are found in
the vicinity of other cell types, and in what numbers, rather than
concentrating exclusively on the cancerous cells. We then extract novel tissue
phenotypic signatures using statistical measurements about tissue composition.
Such signatures can underpin clinical decisions about the advisability of
various types of adjuvant therapy.
Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization
Authors: Chia-Hsiang Lin, Ruiyuan Wu, Wing-Kin Ma, Chong-Yung Chi, Yue Wang
Date: 2017-08-09 00:00:00
Tasks: Hyperspectral Unmixing

Consider a structured matrix factorization model where one factor is
restricted to have its columns lying in the unit simplex. This
simplex-structured matrix factorization (SSMF) model and the associated
factorization techniques have spurred much interest in research topics over
different areas, such as hyperspectral unmixing in remote sensing, topic
discovery in machine learning, to name a few. In this paper we develop a new
theoretical SSMF framework whose idea is to study a maximum volume ellipsoid
inscribed in the convex hull of the data points. This maximum volume inscribed
ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a
sufficient condition under which the MVIE framework guarantees exact recovery
of the factors. The sufficient recovery condition we show for MVIE is much more
relaxed than that of separable non-negative matrix factorization (or pure-pixel
search); coincidentally it is also identical to that of minimum volume
enclosing simplex, which is known to be a powerful SSMF framework for
non-separable problem instances. We also show that MVIE can be practically
implemented by performing facet enumeration and then by solving a convex
optimization problem. The potential of the MVIE framework is illustrated by
numerical results.
Variational Reflectance Estimation from Multi-view Images
Authors: Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers
Date: 2017-09-25 00:00:00

We tackle the problem of reflectance estimation from a set of multi-view
images, assuming known geometry. The approach we put forward turns the input
images into reflectance maps, through a robust variational method. The
variational model comprises an image-driven fidelity term and a term which
enforces consistency of the reflectance estimates with respect to each view. If
illumination is fixed across the views, then reflectance estimation remains
under-constrained: a regularization term, which ensures piecewise-smoothness of
the reflectance, is thus used. Reflectance is parameterized in the image
domain, rather than on the surface, which makes the numerical solution much
easier, by resorting to an alternating majorization-minimization approach.
Experiments on both synthetic and real datasets are carried out to validate the
proposed strategy.
Generalized two-dimensional linear discriminant analysis with regularization
Authors: Chun-Na Li, Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Nai-Yang Deng
Date: 2018-01-23 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Valence Prediction

Recent advances show that two-dimensional linear discriminant analysis
(2DLDA) is a successful matrix based dimensionality reduction method. However,
2DLDA may encounter the singularity issue theoretically and the sensitivity to
outliers. In this paper, a generalized Lp-norm 2DLDA framework with
regularization for an arbitrary $p>0$ is proposed, named G2DLDA. There are
mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary
Lp-norm to measure the between-class and within-class scatter, and hence a
proper $p$ can be selected to achieve the robustness. The other one is that by
introducing an extra regularization term, G2DLDA achieves better generalization
performance, and solves the singularity problem. In addition, G2DLDA can be
solved through a series of convex problems with equality constraint, and it has
closed solution for each single problem. Its convergence can be guaranteed
theoretically when $1\leq p\leq2$. Preliminary experimental results on three
contaminated human face databases show the effectiveness of the proposed
G2DLDA.
Toward Controlled Generation of Text
Authors: Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Attribute, Sentence

Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated semantics. We propose a new neural generative model which combines
variational auto-encoders and holistic attribute discriminators for effective
imposition of semantic structures. With differentiable approximation to
discrete text samples, explicit constraints on independent attribute controls,
and efficient collaborative learning of generator and discriminators, our model
learns highly interpretable representations from even only word annotations,
and produces realistic sentences with desired attributes. Quantitative
evaluation validates the accuracy of sentence and attribute generation.
Approximation of Functions over Manifolds: A Moving Least-Squares Approach
Authors: Barak Sober, Yariv Aizenbud, David Levin
Date: 2017-11-02 00:00:00
Tasks: Dimensionality Reduction

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. We use the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated directly on that point. We prove that our construction yields a smooth function, and in case of noiseless samples the approximation order is $\mathcal{O}(h^{m+1})$, where $h$ is a local density of sample parameter (i.e., the fill distance) and $m$ is the degree of a local polynomial approximation, used in our algorithm. In addition, the proposed algorithm has linear time complexity with respect to the ambient-space's dimension. Thus, we are able to avoid the computational complexity, commonly encountered in high dimensional approximations, without having to perform non-linear dimension reduction, which inevitably introduces distortions to the geometry of the data. Additionaly, we show numerical experiments that the proposed approach compares favorably to statistical approaches for regression over manifolds and show its potential.