pwc-archive/papers-with-abstracts huggingface dataset:
Learned Perceptual Image Enhancement
Authors: Hossein Talebi, Peyman Milanfar
Date: 2017-12-07 00:00:00
Tasks: Image Enhancement, Tone Mapping

Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing.
BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations
Authors: Rhicheek Patra, Egor Samosvat, Michael Roizner, Andrei Mishchenko
Date: 2017-11-15 00:00:00

Recommenders have become widely popular in recent years because of their
broader applicability in many e-commerce applications. These applications rely
on recommenders for generating advertisements for various offers or providing
content recommendations. However, the quality of the generated recommendations
depends on user features (like demography, temporality), offer features (like
popularity, price), and user-offer features (like implicit or explicit
feedback). Current state-of-the-art recommenders do not explore such diverse
features concurrently while generating the recommendations.
  In this paper, we first introduce the notion of Trackers which enables us to
capture the above-mentioned features and thus incorporate users' online
behaviour through statistical aggregates of different features (demography,
temporality, popularity, price). We also show how to capture offer-to-offer
relations, based on their consumption sequence, leveraging neural embeddings
for offers in our Offer2Vec algorithm. We then introduce BoostJet, a novel
recommender which integrates the Trackers along with the neural embeddings
using MatrixNet, an efficient distributed implementation of gradient boosted
decision tree, to improve the recommendation quality significantly. We provide
an in-depth evaluation of BoostJet on Yandex's dataset, collecting online
behaviour from tens of millions of online users, to demonstrate the
practicality of BoostJet in terms of recommendation quality as well as
scalability.
Sequences, Items And Latent Links: Recommendation With Consumed Item Packs
Authors: Rachid Guerraoui, Erwan Le Merrer, Rhicheek Patra, Jean-Ronan Vigouroux
Date: 2017-11-16 00:00:00
Tasks: Collaborative Filtering

Recommenders personalize the web content by typically using collaborative
filtering to relate users (or items) based on explicit feedback, e.g., ratings.
The difficulty of collecting this feedback has recently motivated to consider
implicit feedback (e.g., item consumption along with the corresponding time).
  In this paper, we introduce the notion of consumed item pack (CIP) which
enables to link users (or items) based on their implicit analogous consumption
behavior. Our proposal is generic, and we show that it captures three novel
implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word
embedding-based (DEEPCIP), as well as a state-of-the-art technique using
implicit feedback (FISM). We show that our recommenders handle incremental
updates incorporating freshly consumed items. We demonstrate that all three
recommenders provide a recommendation quality that is competitive with
state-of-the-art ones, including one incorporating both explicit and implicit
feedback.
End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech
Authors: Hai X. Pham, Yuting Wang, Vladimir Pavlovic
Date: 2017-10-02 00:00:00
Tasks: Face Model

We present a deep learning framework for real-time speech-driven 3D facial
animation from just raw waveforms. Our deep neural network directly maps an
input sequence of speech audio to a series of micro facial action unit
activations and head rotations to drive a 3D blendshape face model. In
particular, our deep model is able to learn the latent representations of
time-varying contextual information and affective states within the speech.
Hence, our model not only activates appropriate facial action units at
inference to depict different utterance generating actions, in the form of lip
movements, but also, without any assumption, automatically estimates emotional
intensity of the speaker and reproduces her ever-changing affective states by
adjusting strength of facial unit activations. For example, in a happy speech,
the mouth opens wider than normal, while other facial units are relaxed; or in
a surprised state, both eyebrows raise higher. Experiments on a diverse
audiovisual corpus of different actors across a wide range of emotional states
show interesting and promising results of our approach. Being
speaker-independent, our generalized model is readily applicable to various
tasks in human-machine interaction and animation.
Per-Pixel Feedback for improving Semantic Segmentation
Authors: Aditya Ganeshan
Date: 2017-12-07 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation is the task of assigning a label to each pixel in the
image.In recent years, deep convolutional neural networks have been driving
advances in multiple tasks related to cognition. Although, DCNNs have resulted
in unprecedented visual recognition performances, they offer little
transparency. To understand how DCNN based models work at the task of semantic
segmentation, we try to analyze the DCNN models in semantic segmentation. We
try to find the importance of global image information for labeling pixels.
  Based on the experiments on discriminative regions, and modeling of
fixations, we propose a set of new training loss functions for fine-tuning DCNN
based models. The proposed training regime has shown improvement in performance
of DeepLab Large FOV(VGG-16) Segmentation model for PASCAL VOC 2012 dataset.
However, further test remains to conclusively evaluate the benefits due to the
proposed loss functions across models, and data-sets.
  Submitted in part fulfillment of the requirements for the degree of
Integrated Masters of Science in Applied Mathematics.
  Update: Further Experiment showed minimal benefits.
  Code Available [here](https://github.com/BardOfCodes/Seg-Unravel).
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
Authors: Sen Zhao, Daniela Witten, Ali Shojaie
Date: 2017-05-16 00:00:00
Tasks: regression, valid

A great deal of interest has recently focused on conducting inference on the parameters in a high-dimensional linear model. In this paper, we consider a simple and very na\"{i}ve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set. Conventional statistical wisdom tells us that we cannot make use of the standard statistical inference tools for the resulting least squares model (such as confidence intervals and $p$-values), since we peeked at the data twice: once in running the lasso, and again in fitting the least squares model. However, in this paper, we show that under a certain set of assumptions, with high probability, the set of variables selected by the lasso is identical to the one selected by the noiseless lasso and is hence deterministic. Consequently, the na\"{i}ve two-step approach can yield asymptotically valid inference. We utilize this finding to develop the \emph{na\"ive confidence interval}, which can be used to draw inference on the regression coefficients of the model selected by the lasso, as well as the \emph{na\"ive score test}, which can be used to test the hypotheses regarding the full-model regression coefficients.
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures
Authors: Mohammadreza Mohaghegh Neyshabouri, Kaan Gokcesu, Huseyin Ozkan, Suleyman S. Kozat
Date: 2016-12-05 00:00:00
Tasks: Multi-class Classification

We propose online algorithms for sequential learning in the contextual
multi-armed bandit setting. Our approach is to partition the context space and
then optimally combine all of the possible mappings between the partition
regions and the set of bandit arms in a data driven manner. We show that in our
approach, the best mapping is able to approximate the best arm selection policy
to any desired degree under mild Lipschitz conditions. Therefore, we design our
algorithms based on the optimal adaptive combination and asymptotically achieve
the performance of the best mapping as well as the best arm selection policy.
This optimality is also guaranteed to hold even in adversarial environments
since we do not rely on any statistical assumptions regarding the contexts or
the loss of the bandit arms. Moreover, we design efficient implementations for
our algorithms in various hierarchical partitioning structures such as
lexicographical or arbitrary position splitting and binary trees (and several
other partitioning examples). For instance, in the case of binary tree
partitioning, the computational complexity is only log-linear in the number of
regions in the finest partition. In conclusion, we provide significant
performance improvements by introducing upper bounds (w.r.t. the best arm
selection policy) that are mathematically proven to vanish in the average loss
per round sense at a faster rate compared to the state-of-the-art. Our
experimental work extensively covers various scenarios ranging from bandit
settings to multi-class classification with real and synthetic data. In these
experiments, we show that our algorithms are highly superior over the
state-of-the-art techniques while maintaining the introduced mathematical
guarantees and a computationally decent scalability.
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Authors: Ayush Tewari, Michael Zollhöfer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Pérez, Christian Theobalt
Date: 2017-03-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder, Face Reconstruction, Monocular Reconstruction

In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.
Bayesian Paragraph Vectors
Authors: Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt
Date: 2017-11-10 00:00:00
Tasks: Sentiment Analysis, Word Embeddings

Word2vec (Mikolov et al., 2013) has proven to be successful in natural
language processing by capturing the semantic relationships between different
words. Built on top of single-word embeddings, paragraph vectors (Le and
Mikolov, 2014) find fixed-length representations for pieces of text with
arbitrary lengths, such as documents, paragraphs, and sentences. In this work,
we propose a novel interpretation for neural-network-based paragraph vectors by
developing an unsupervised generative model whose maximum likelihood solution
corresponds to traditional paragraph vectors. This probabilistic formulation
allows us to go beyond point estimates of parameters and to perform Bayesian
posterior inference. We find that the entropy of paragraph vectors decreases
with the length of documents, and that information about posterior uncertainty
improves performance in supervised learning tasks such as sentiment analysis
and paraphrase detection.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Authors: Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, Wojciech Samek
Date: 2016-12-06 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features.
The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences
Authors: Yuxin Chen, Emmanuel Candes
Date: 2016-09-19 00:00:00

Various applications involve assigning discrete label values to a collection
of objects based on some pairwise noisy data. Due to the discrete---and hence
nonconvex---structure of the problem, computing the optimal assignment
(e.g.~maximum likelihood assignment) becomes intractable at first sight. This
paper makes progress towards efficient computation by focusing on a concrete
joint alignment problem---that is, the problem of recovering $n$ discrete
variables $x_i \in \{1,\cdots, m\}$, $1\leq i\leq n$ given noisy observations
of their modulo differences $\{x_i - x_j~\mathsf{mod}~m\}$. We propose a
low-complexity and model-free procedure, which operates in a lifted space by
representing distinct label values in orthogonal directions, and which attempts
to optimize quadratic functions over hypercubes. Starting with a first guess
computed via a spectral method, the algorithm successively refines the iterates
via projected power iterations. We prove that for a broad class of statistical
models, the proposed projected power method makes no error---and hence
converges to the maximum likelihood estimate---in a suitable regime. Numerical
experiments have been carried out on both synthetic and real data to
demonstrate the practicality of our algorithm. We expect this algorithmic
framework to be effective for a broad range of discrete assignment problems.
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
Authors: Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Date: 2017-12-07 00:00:00
Tasks: Image Generation, Image Reconstruction, Uncertainty Quantification

Stochastic image reconstruction is a key part of modern digital rock physics
and materials analysis that aims to create numerous representative samples of
material micro-structures for upscaling, numerical computation of effective
properties and uncertainty quantification. We present a method of
three-dimensional stochastic image reconstruction based on generative
adversarial neural networks (GANs). GANs represent a framework of unsupervised
learning methods that require no a priori inference of the probability
distribution associated with the training data. Using a fully convolutional
neural network allows fast sampling of large volumetric images.We apply a GAN
based workflow of network training and image generation to an oolitic Ketton
limestone micro-CT dataset. Minkowski functionals, effective permeability as
well as velocity distributions of simulated flow within the acquired images are
compared with the synthetic reconstructions generated by the deep neural
network. While our results show that GANs allow a fast and accurate
reconstruction of the evaluated image dataset, we address a number of open
questions and challenges involved in the evaluation of generative network-based
methods.
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Authors: Li Zhou, Kevin Small, Oleg Rokhlenko, Charles Elkan
Date: 2017-12-07 00:00:00
Tasks: Decoder, Goal-Oriented Dialog, Offline RL, Reinforcement Learning, Reinforcement Learning (RL)

Learning a goal-oriented dialog policy is generally performed offline with
supervised learning algorithms or online with reinforcement learning (RL).
Additionally, as companies accumulate massive quantities of dialog transcripts
between customers and trained human agents, encoder-decoder methods have gained
popularity as agent utterances can be directly treated as supervision without
the need for utterance-level annotations. However, one potential drawback of
such approaches is that they myopically generate the next agent utterance
without regard for dialog-level considerations. To resolve this concern, this
paper describes an offline RL method for learning from unannotated corpora that
can optimize a goal-oriented policy at both the utterance and dialog level. We
introduce a novel reward function and use both on-policy and off-policy policy
gradient to learn a policy offline without requiring online user interaction or
an explicit state space definition.
RelNN: A Deep Neural Model for Relational Learning
Authors: Seyed Mehran Kazemi, David Poole
Date: 2017-12-07 00:00:00
Tasks: model, regression, Relational Reasoning

Statistical relational AI (StarAI) aims at reasoning and learning in noisy
domains described in terms of objects and relationships by combining
probability with first-order logic. With huge advances in deep learning in the
current years, combining deep networks with first-order logic has been the
focus of several recent studies. Many of the existing attempts, however, only
focus on relations and ignore object properties. The attempts that do consider
object properties are limited in terms of modelling power or scalability. In
this paper, we develop relational neural networks (RelNNs) by adding hidden
layers to relational logistic regression (the relational counterpart of
logistic regression). We learn latent properties for objects both directly and
through general rules. Back-propagation is used for training these models. A
modular, layer-wise architecture facilitates utilizing the techniques developed
within deep learning community to our architecture. Initial experiments on
eight tasks over three real-world datasets show that RelNNs are promising
models for relational learning.
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition
Authors: Ricardo Gamelas Sousa, Jorge M. Santos, Luís M. Silva, Luís A. Alexandre, Tiago Esteves, Sara Rocha, Paulo Monjardino, Joaquim Marques de Sá, Francisco Figueiredo, Pedro Quelhas
Date: 2017-12-07 00:00:00
Tasks: Denoising, Quantization, Transfer Learning

In this paper we present a system for the detection of immunogold particles
and a Transfer Learning (TL) framework for the recognition of these immunogold
particles. Immunogold particles are part of a high-magnification method for the
selective localization of biological molecules at the subcellular level only
visible through Electron Microscopy. The number of immunogold particles in the
cell walls allows the assessment of the differences in their compositions
providing a tool to analise the quality of different plants. For its
quantization one requires a laborious manual labeling (or annotation) of images
containing hundreds of particles. The system that is proposed in this paper can
leverage significantly the burden of this manual task.
  For particle detection we use a LoG filter coupled with a SDA. In order to
improve the recognition, we also study the applicability of TL settings for
immunogold recognition. TL reuses the learning model of a source problem on
other datasets (target problems) containing particles of different sizes. The
proposed system was developed to solve a particular problem on maize cells,
namely to determine the composition of cell wall ingrowths in endosperm
transfer cells. This novel dataset as well as the code for reproducing our
experiments is made publicly available.
  We determined that the LoG detector alone attained more than 84\% of accuracy
with the F-measure. Developing immunogold recognition with TL also provided
superior performance when compared with the baseline models augmenting the
accuracy rates by 10\%.
Hybrid eye center localization using cascaded regression and hand-crafted model fitting
Authors: Alex Levinshtein, Edmund Phung, Parham Aarabi
Date: 2017-12-07 00:00:00
Tasks: regression

We propose a new cascaded regressor for eye center detection. Previous
methods start from a face or an eye detector and use either advanced features
or powerful regressors for eye center localization, but not both. Instead, we
detect the eyes more accurately using an existing facial feature alignment
method. We improve the robustness of localization by using both advanced
features and powerful regression machinery. Unlike most other methods that do
not refine the regression results, we make the localization more accurate by
adding a robust circle fitting post-processing step. Finally, using a simple
hand-crafted method for eye center localization, we show how to train the
cascaded regressor without the need for manually annotated training data. We
evaluate our new approach and show that it achieves state-of-the-art
performance on the BioID, GI4E, and the TalkingFace datasets. At an average
normalized error of e < 0.05, the regressor trained on manually annotated data
yields an accuracy of 95.07% (BioID), 99.27% (GI4E), and 95.68% (TalkingFace).
The automatically trained regressor is nearly as good, yielding an accuracy of
93.9% (BioID), 99.27% (GI4E), and 95.46% (TalkingFace).
The Shape of an Image: A Study of Mapper on Images
Authors: Alejandro Robles, Mustafa Hajij, Paul Rosen
Date: 2017-10-24 00:00:00

We study the topological construction called Mapper in the context of simply
connected domains, in particular on images. The Mapper construction can be
considered as a generalization for contour, split, and joint trees on simply
connected domains. A contour tree on an image domain assumes the height
function to be a piecewise linear Morse function. This is a rather restrictive
class of functions and does not allow us to explore the topology for most real
world images. The Mapper construction avoids this limitation by assuming only
continuity on the height function allowing this construction to robustly deal
with a significant larger set of images. We provide a customized construction
for Mapper on images, give a fast algorithm to compute it, and show how to
simplify the Mapper structure in this case. Finally, we provide a simple
procedure that guarantees the equivalence of Mapper to contour, join, and split
trees on a simply connected domain.