pwc-archive/papers-with-abstracts huggingface dataset:
DeepLens: Shallow Depth Of Field From A Single Image
Authors: Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, Huchuan Lu
Date: 2018-10-18 00:00:00
Tasks: Depth Estimation, Depth Prediction

We aim to generate high resolution shallow depth-of-field (DoF) images from a
single all-in-focus image with controllable focal distance and aperture size.
To achieve this, we propose a novel neural network model comprised of a depth
prediction module, a lens blur module, and a guided upsampling module. All
modules are differentiable and are learned from data. To train our depth
prediction module, we collect a dataset of 2462 RGB-D images captured by mobile
phones with a dual-lens camera, and use existing segmentation datasets to
improve border prediction. We further leverage a synthetic dataset with known
depth to supervise the lens blur and guided upsampling modules. The
effectiveness of our system and training strategies are verified in the
experiments. Our method can generate high-quality shallow DoF images at high
resolution, and produces significantly fewer artifacts than the baselines and
existing solutions for single image shallow DoF synthesis. Compared with the
iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on
a dual-lens depth camera, our method generates comparable results, while
allowing for greater flexibility to choose focal points and aperture size, and
is not limited to one capture setup.
Stochastic Distance Transform
Authors: Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
Date: 2018-10-18 00:00:00
Tasks: Template Matching

The distance transform (DT) and its many variations are ubiquitous tools for
image processing and analysis. In many imaging scenarios, the images of
interest are corrupted by noise. This has a strong negative impact on the
accuracy of the DT, which is highly sensitive to spurious noise points. In this
study, we consider images represented as discrete random sets and observe
statistics of DT computed on such representations. We, thus, define a
stochastic distance transform (SDT), which has an adjustable robustness to
noise. Both a stochastic Monte Carlo method and a deterministic method for
computing the SDT are proposed and compared. Through a series of empirical
tests, we demonstrate that the SDT is effective not only in improving the
accuracy of the computed distances in the presence of noise, but also in
improving the performance of template matching and watershed segmentation of
partially overlapping objects, which are examples of typical applications where
DTs are utilized.
Good Initializations of Variational Bayes for Deep Models
Authors: Simone Rossi, Pietro Michiardi, Maurizio Filippone
Date: 2018-10-18 00:00:00
Tasks: Bayesian Inference, General Classification, regression, Variational Inference

Stochastic variational inference is an established way to carry out
approximate Bayesian inference for deep models. While there have been effective
proposals for good initializations for loss minimization in deep learning, far
less attention has been devoted to the issue of initialization of stochastic
variational inference. We address this by proposing a novel layer-wise
initialization strategy based on Bayesian linear models. The proposed method is
extensively validated on regression and classification tasks, including
Bayesian DeepNets and ConvNets, showing faster and better convergence compared
to alternatives inspired by the literature on initializations for loss
minimization.
Discourse Embellishment Using a Deep Encoder-Decoder Network
Authors: Leonid Berov, Kai Standvoss
Date: 2018-10-18 00:00:00
Proceeding: WS 2018 11
Tasks: Decoder

We suggest a new NLG task in the context of the discourse generation pipeline
of computational storytelling systems. This task, textual embellishment, is
defined by taking a text as input and generating a semantically equivalent
output with increased lexical and syntactic complexity. Ideally, this would
allow the authors of computational storytellers to implement just lightweight
NLG systems and use a domain-independent embellishment module to translate its
output into more literary text. We present promising first results on this task
using LSTM Encoder-Decoder networks trained on the WikiLarge dataset.
Furthermore, we introduce "Compiled Computer Tales", a corpus of
computationally generated stories, that can be used to test the capabilities of
embellishment algorithms.
Finding Average Regret Ratio Minimizing Set in Database
Authors: Sepanta Zeighami, Raymong Chi-Wing Wong
Date: 2018-10-18 00:00:00

Selecting a certain number of data points (or records) from a database which
"best" satisfy users' expectations is a very prevalent problem with many
applications. One application is a hotel booking website showing a certain
number of hotels on a single page. However, this problem is very challenging
since the selected points should "collectively" satisfy the expectation of all
users. Showing a certain number of data points to a single user could decrease
the satisfaction of a user because the user may not be able to see his/her
favorite point which could be found in the original database. In this paper, we
would like to find a set of k points such that on average, the satisfaction
(ratio) of a user is maximized. This problem takes into account the probability
distribution of the users and considers the satisfaction (ratio) of all users,
which is more reasonable in practice, compared with the existing studies that
only consider the worst-case satisfaction (ratio) of the users, which may not
reflect the whole population and is not useful in some applications. Motivated
by this, in this paper, we propose algorithms for this problem. Finally, we
conducted experiments to show the effectiveness and the efficiency of the
algorithms.
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
Authors: Bolun Zheng, Yaowu Chen, Xiang Tian, Fan Zhou, Xuesong Liu
Date: 2018-10-18 00:00:00
Tasks: Color Image Compression Artifact Reduction, Image Compression, Image Compression Artifact Reduction, JPEG Artifact Correction, Quantization, Translation

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different. Moreover, these methods train a specific model for each compression quality and require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with the pixel position labeling map and the quantization tables as inputs. Specifically, we proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN. A dense block was introduced to improve the performance of extractor in DRU. The implicit dual-domain translation allows the IDCN to handle color images with the discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with little performance sacrifice and demonstrates great potential for practical applications.
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Authors: Taiji Suzuki
Date: 2018-10-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Learning

Deep learning has shown high performances in various types of tasks from
visual recognition to natural language processing, which indicates superior
flexibility and adaptivity of deep learning. To understand this phenomenon
theoretically, we develop a new approximation and estimation error analysis of
deep learning with the ReLU activation for functions in a Besov space and its
variant with mixed smoothness. The Besov space is a considerably general
function space including the Holder space and Sobolev space, and especially can
capture spatial inhomogeneity of smoothness. Through the analysis in the Besov
space, it is shown that deep learning can achieve the minimax optimal rate and
outperform any non-adaptive (linear) estimator such as kernel ridge regression,
which shows that deep learning has higher adaptivity to the spatial
inhomogeneity of the target function than other estimators such as linear ones.
In addition to this, it is shown that deep learning can avoid the curse of
dimensionality if the target function is in a mixed smooth Besov space. We also
show that the dependency of the convergence rate on the dimensionality is tight
due to its minimax optimality. These results support high adaptivity of deep
learning and its superior ability as a feature extractor.
Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection
Authors: Yulia S. Chernyshova, Mikhail A. Aliev, Ekaterina S. Gushchanskaia, Alexander V. Sheshkus
Date: 2018-10-18 00:00:00
Tasks: Font Recognition, Multi-Task Learning, Specificity

In this paper, we consider the problem of detecting counterfeit identity
documents in images captured with smartphones. As the number of documents
contain special fonts, we study the applicability of convolutional neural
networks (CNNs) for detection of the conformance of the fonts used with the
ones, corresponding to the government standards. Here, we use multi-task
learning to differentiate samples by both fonts and characters and compare the
resulting classifier with its analogue trained for binary font classification.
We train neural networks for authenticity estimation of the fonts used in
machine-readable zones and ID numbers of the Russian national passport and test
them on samples of individual characters acquired from 3238 images of the
Russian national passport. Our results show that the usage of multi-task
learning increases sensitivity and specificity of the classifier. Moreover, the
resulting CNNs demonstrate high generalization ability as they correctly
classify fonts which were not present in the training set. We conclude that the
proposed method is sufficient for authentication of the fonts and can be used
as a part of the forgery detection system for images acquired with a smartphone
camera.
Variational Noise-Contrastive Estimation
Authors: Benjamin Rhodes, Michael Gutmann
Date: 2018-10-18 00:00:00
Tasks: parameter estimation, Variational Inference

Unnormalised latent variable models are a broad and flexible class of
statistical models. However, learning their parameters from data is
intractable, and few estimation techniques are currently available for such
models. To increase the number of techniques in our arsenal, we propose
variational noise-contrastive estimation (VNCE), building on NCE which is a
method that only applies to unnormalised models. The core idea is to use a
variational lower bound to the NCE objective function, which can be optimised
in the same fashion as the evidence lower bound (ELBO) in standard variational
inference (VI). We prove that VNCE can be used for both parameter estimation of
unnormalised models and posterior inference of latent variables. The developed
theory shows that VNCE has the same level of generality as standard VI, meaning
that advances made there can be directly imported to the unnormalised setting.
We validate VNCE on toy models and apply it to a realistic problem of
estimating an undirected graphical model from incomplete data.
An Upper Bound for Random Measurement Error in Causal Discovery
Authors: Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
Date: 2018-10-18 00:00:00
Tasks: Causal Discovery

Causal discovery algorithms infer causal relations from data based on several
assumptions, including notably the absence of measurement error. However, this
assumption is most likely violated in practical applications, which may result
in erroneous, irreproducible results. In this work we show how to obtain an
upper bound for the variance of random measurement error from the covariance
matrix of measured variables and how to use this upper bound as a correction
for constraint-based causal discovery. We demonstrate a practical application
of our approach on both simulated data and real-world protein signaling data.
LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer
Authors: Simmi Mourya, Sonaal Kant, Pulkit Kumar, Anubha Gupta, Ritu Gupta
Date: 2018-10-18 00:00:00
Tasks: All, General Classification

Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the
pediatric cancers. In general, the task of identifying immature leukemic blasts
from normal cells under the microscope is challenging because morphologically
the images of the two cells appear similar. In this paper, we propose a deep
learning framework for classifying immature leukemic blasts and normal cells.
The proposed model combines the Discrete Cosine Transform (DCT) domain features
extracted via CNN with the Optical Density (OD) space features to build a
robust classifier. Elaborate experiments have been conducted to validate the
proposed LeukoNet classifier.
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Authors: Bolun Zheng, Rui Sun, Xiang Tian, Yaowu Chen
Date: 2018-10-18 00:00:00
Tasks: GPU, JPEG Artifact Correction, Jpeg Compression Artifact Reduction

Recent studies have used deep residual convolutional neural networks (CNNs)
for JPEG compression artifact reduction. This study proposes a scalable CNN
called S-Net. Our approach effectively adjusts the network scale dynamically in
a multitask system for real-time operation with little performance loss. It
offers a simple and direct technique to evaluate the performance gains obtained
with increasing network depth, and it is helpful for removing redundant network
layers to maximize the network efficiency. We implement our architecture using
the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We
train our models on the DIV2K dataset and evaluate their performance on public
benchmark datasets. To validate the generality and universality of the proposed
method, we created and utilized a new dataset, called WIN143, for
over-processed images evaluation. Experimental results indicate that our
proposed approach outperforms other CNN-based methods and achieves
state-of-the-art performance.
HierLPR: Decision making in hierarchical multi-label classification with local precision rates
Authors: Christine Ho, Yuting Ye, Ci-Ren Jiang, Wayne Tai Lee, Haiyan Huang
Date: 2018-10-18 00:00:00
Tasks: Decision Making, General Classification, Hierarchical Multi-label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this article we propose a novel ranking algorithm, referred to as HierLPR,
for the multi-label classification problem when the candidate labels follow a
known hierarchical structure. HierLPR is motivated by a new metric called eAUC
that we design to assess the ranking of classification decisions. This metric,
associated with the hit curve and local precision rate, emphasizes the accuracy
of the first calls. We show that HierLPR optimizes eAUC under the tree
constraint and some light assumptions on the dependency between the nodes in
the hierarchy. We also provide a strategy to make calls for each node based on
the ordering produced by HierLPR, with the intent of controlling FDR or
maximizing F-score. The performance of our proposed methods is demonstrated on
synthetic datasets as well as a real example of disease diagnosis using NCBI
GEO datasets. In these cases, HierLPR shows a favorable result over competing
methods in the early part of the precision-recall curve.
A Temporally Sensitive Submodularity Framework for Timeline Summarization
Authors: Sebastian Martschat, Katja Markert
Date: 2018-10-18 00:00:00
Proceeding: CONLL 2018 10
Tasks: Document Summarization, Multi-Document Summarization, Timeline Summarization

Timeline summarization (TLS) creates an overview of long-running events via
dated daily summaries for the most important dates. TLS differs from standard
multi-document summarization (MDS) in the importance of date selection,
interdependencies between summaries of different dates and by having very short
summaries compared to the number of corpus documents. However, we show that MDS
optimization models using submodular functions can be adapted to yield
well-performing TLS models by designing objective functions and constraints
that model the temporal dimension inherent in TLS. Importantly, these
adaptations retain the elegance and advantages of the original MDS models
(clear separation of features and inference, performance guarantees and
scalability, little need for supervision) that current TLS-specific models
lack. An open-source implementation of the framework and all models described
in this paper is available online.
Accurate and Scalable Image Clustering Based On Sparse Representation of Camera Fingerprint
Authors: Quoc-Tin Phan, Giulia Boato, Francesco G. B. De Natale
Date: 2018-10-18 00:00:00
Tasks: Clustering, Image Clustering

Clustering images according to their acquisition devices is a well-known
problem in multimedia forensics, which is typically faced by means of camera
Sensor Pattern Noise (SPN). Such an issue is challenging since SPN is a
noise-like signal, hard to be estimated and easy to be attenuated or destroyed
by many factors. Moreover, the high dimensionality of SPN hinders large-scale
applications. Existing approaches are typically based on the correlation among
SPNs in the pixel domain, which might not be able to capture intrinsic data
structure in union of vector subspaces. In this paper, we propose an accurate
clustering framework, which exploits linear dependencies among SPNs in their
intrinsic vector subspaces. Such dependencies are encoded under sparse
representations which are obtained by solving a LASSO problem with
non-negativity constraint. The proposed framework is highly accurate in number
of clusters estimation and image association. Moreover, our framework is
scalable to the number of images and robust against double JPEG compression as
well as the presence of outliers, owning big potential for real-world
applications. Experimental results on Dresden and Vision database show that our
proposed framework can adapt well to both medium-scale and large-scale
contexts, and outperforms state-of-the-art methods.
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
Authors: Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis
Date: 2018-10-18 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Constituency Parsing, Semantic Parsing, slot-filling, Slot Filling

Task oriented dialog systems typically first parse user utterances to
semantic frames comprised of intents and slots. Previous work on task oriented
intent and slot-filling work has been restricted to one intent per query and
one slot label per token, and thus cannot model complex compositional requests.
Alternative semantic parsing systems have represented queries as logical forms,
but these are challenging to annotate and parse. We propose a hierarchical
annotation scheme for semantic parsing that allows the representation of
compositional queries, and can be efficiently and accurately parsed by standard
constituency parsing models. We release a dataset of 44k annotated queries
(fb.me/semanticparsingdialog), and show that parsing models outperform
sequence-to-sequence approaches on this dataset.
Unsupervised Neural Text Simplification
Authors: Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan
Date: 2018-10-18 00:00:00
Proceeding: ACL 2019 7
Tasks: Denoising, Text Simplification

The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders and gains knowledge of simplification through discrimination based-losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on a public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. Addition of a few labelled pairs also improves the performance further.
Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach
Authors: Avisek Lahiri, Abhinav Agarwalla, Prabir Kumar Biswas
Date: 2018-10-18 00:00:00
Tasks: Domain Adaptation, Gaze Estimation, Unsupervised Domain Adaptation

With contemporary advancements of graphics engines, recent trend in deep
learning community is to train models on automatically annotated simulated
examples and apply on real data during test time. This alleviates the burden of
manual annotation. However, there is an inherent difference of distributions
between images coming from graphics engine and real world. Such domain
difference deteriorates test time performances of models trained on synthetic
examples. In this paper we address this issue with unsupervised adversarial
feature adaptation across synthetic and real domain for the special use case of
eye gaze estimation which is an essential component for various downstream HCI
tasks. We initially learn a gaze estimator on annotated synthetic samples
rendered from a 3D game engine and then adapt the features of unannotated real
samples via a zero-sum minmax adversarial game against a domain discriminator
following the recent paradigm of generative adversarial networks. Such
adversarial adaptation forces features of both domains to be indistinguishable
which enables us to use regression models trained on synthetic domain to be
used on real samples. On the challenging MPIIGaze real life dataset, we
outperform recent fully supervised methods trained on manually annotated real
samples by appreciable margins and also achieve 13\% more relative gain after
adaptation compared to the current benchmark method of SimGAN
Explaining Machine Learning Models using Entropic Variable Projection
Authors: François Bachoc, Fabrice Gamboa, Max Halford, Jean-Michel Loubes, Laurent Risser
Date: 2018-10-18 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence, we propose a group explainability formalism for trained machine learning decision rules, based on their response to the variability of the input variables distribution. In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections. This is thus the first unified and model agnostic formalism enabling data scientists to interpret the dependence between the input variables, their impact on the prediction errors, and their influence on the output predictions. Convergence rates of the entropic projections are provided in the large sample case. Most importantly, we prove that computing an explanation in our framework has a low algorithmic complexity, making it scalable to real-life large datasets. We illustrate our strategy by explaining complex decision rules learned by using XGBoost, Random Forest or Deep Neural Network classifiers on various datasets such as Adult Income, MNIST, CelebA, Boston Housing, Iris, as well as synthetic ones. We finally make clear its differences with the explainability strategies LIME and SHAP, that are based on single observations. Results can be reproduced by using the freely distributed Python toolbox https://gems-ai.aniti.fr/.