pwc-archive/papers-with-abstracts huggingface dataset:
On the Capacity of Face Representation
Authors: Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain
Date: 2017-09-29 00:00:00
Tasks: Face Recognition

In this paper we address the following question, given a face representation,
how many identities can it resolve? In other words, what is the capacity of the
face representation? A scientific basis for estimating the capacity of a given
face representation will not only benefit the evaluation and comparison of
different representation methods, but will also establish an upper bound on the
scalability of an automatic face recognition system. We cast the face capacity
problem in terms of packing bounds on a low-dimensional manifold embedded
within a deep representation space. By explicitly accounting for the manifold
structure of the representation as well two different sources of
representational noise: epistemic (model) uncertainty and aleatoric (data)
variability, our approach is able to estimate the capacity of a given face
representation. To demonstrate the efficacy of our approach, we estimate the
capacity of two deep neural network based face representations, namely
128-dimensional FaceNet and 512-dimensional SphereFace. Numerical experiments
on unconstrained faces (IJB-C) provides a capacity upper bound of
$2.7\times10^4$ for FaceNet and $8.4\times10^4$ for SphereFace representation
at a false acceptance rate (FAR) of 1%. As expected, capacity reduces
drastically at lower FARs. The capacity at FAR of 0.1% and 0.001% is
$2.2\times10^3$ and $1.6\times10^{1}$, respectively for FaceNet and
$3.6\times10^3$ and $6.0\times10^0$, respectively for SphereFace.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Authors: Davy Neven, Bert de Brabandere, Stamatios Georgoulis, Marc Proesmans, Luc van Gool
Date: 2018-02-15 00:00:00
Tasks: Instance Segmentation, Lane Detection, Semantic Segmentation

Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results.
Convolutional Analysis Operator Learning: Acceleration and Convergence
Authors: Il Yong Chun, Jeffrey A. Fessler
Date: 2018-02-15 00:00:00
Tasks: Dictionary Learning, Operator learning

Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets -- particularly with multi-layered structures, e.g., convolutional neural networks -- or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the "synthesis" signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computational tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.
Tools and resources for Romanian text-to-speech and speech-to-text applications
Authors: Tiberiu Boros, Stefan Daniel Dumitrescu, Vasile Pais
Date: 2018-02-15 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis, Speech-to-Text, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In this paper we introduce a set of resources and tools aimed at providing
support for natural language processing, text-to-speech synthesis and speech
recognition for Romanian. While the tools are general purpose and can be used
for any language (we successfully trained our system for more than 50 languages
and participated in the Universal Dependencies Shared Task), the resources are
only relevant for Romanian language processing.
Improved Complexities of Conditional Gradient-Type Methods with Applications to Robust Matrix Recovery Problems
Authors: Dan Garber, Shoham Sabach, Atara Kaplan
Date: 2018-02-15 00:00:00

Motivated by robust matrix recovery problems such as Robust Principal Component Analysis, we consider a general optimization problem of minimizing a smooth and strongly convex loss function applied to the sum of two blocks of variables, where each block of variables is constrained or regularized individually. We study a Conditional Gradient-Type method which is able to leverage the special structure of the problem to obtain faster convergence rates than those attainable via standard methods, under a variety of assumptions. In particular, our method is appealing for matrix problems in which one of the blocks corresponds to a low-rank matrix since it avoids prohibitive full-rank singular value decompositions required by most standard methods. While our initial motivation comes from problems which originated in statistics, our analysis does not impose any statistical assumptions on the data.
Tree Ensembles with Rule Structured Horseshoe Regularization
Authors: Malte Nalenz, Mattias Villani
Date: 2017-02-16 00:00:00
Tasks: Cancer Classification, General Classification, regression

We propose a new Bayesian model for flexible nonlinear regression and
classification using tree ensembles. The model is based on the RuleFit approach
in Friedman and Popescu (2008) where rules from decision trees and linear terms
are used in a L1-regularized regression. We modify RuleFit by replacing the
L1-regularization by a horseshoe prior, which is well known to give aggressive
shrinkage of noise predictor while leaving the important signal essentially
untouched. This is especially important when a large number of rules are used
as predictors as many of them only contribute noise. Our horseshoe prior has an
additional hierarchical layer that applies more shrinkage a priori to rules
with a large number of splits, and to rules that are only satisfied by a few
observations. The aggressive noise shrinkage of our prior also makes it
possible to complement the rules from boosting in Friedman and Popescu (2008)
with an additional set of trees from random forest, which brings a desirable
diversity to the ensemble. We sample from the posterior distribution using a
very efficient and easily implemented Gibbs sampler. The new model is shown to
outperform state-of-the-art methods like RuleFit, BART and random forest on 16
datasets. The model and its interpretation is demonstrated on the well known
Boston housing data, and on gene expression data for cancer classification. The
posterior sampling, prediction and graphical tools for interpreting the model
results are implemented in a publicly available R package.
Advanced Steel Microstructural Classification by Deep Learning Methods
Authors: Seyed Majid Azimi, Dominik Britz, Michael Engstler, Mario Fritz, Frank Mücklich
Date: 2017-06-20 00:00:00
Tasks: Classification, Deep Learning, General Classification

The inner structure of a material is called microstructure. It stores the
genesis of a material and determines all its physical and chemical properties.
While microstructural characterization is widely spread and well known, the
microstructural classification is mostly done manually by human experts, which
gives rise to uncertainties due to subjectivity. Since the microstructure could
be a combination of different phases or constituents with complex substructures
its automatic classification is very challenging and only a few prior studies
exist. Prior works focused on designed and engineered features by experts and
classified microstructures separately from the feature extraction step.
Recently, Deep Learning methods have shown strong performance in vision
applications by learning the features from data together with the
classification step. In this work, we propose a Deep Learning method for
microstructural classification in the examples of certain microstructural
constituents of low carbon steel. This novel method employs pixel-wise
segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a
max-voting scheme. Our system achieves 93.94% classification accuracy,
drastically outperforming the state-of-the-art method of 48.89% accuracy.
Beyond the strong performance of our method, this line of research offers a
more robust and first of all objective way for the difficult task of steel
quality appreciation.
Natural data structure extracted from neighborhood-similarity graphs
Authors: Tom Lorimer, Karlis Kanders, Ruedi Stoop
Date: 2018-02-15 00:00:00
Tasks: Clustering, Dimensionality Reduction

'Big' high-dimensional data are commonly analyzed in low-dimensions, after
performing a dimensionality-reduction step that inherently distorts the data
structure. For the same purpose, clustering methods are also often used. These
methods also introduce a bias, either by starting from the assumption of a
particular geometric form of the clusters, or by using iterative schemes to
enhance cluster contours, with uncontrollable consequences. The goal of data
analysis should, however, be to encode and detect structural data features at
all scales and densities simultaneously, without assuming a parametric form of
data point distances, or modifying them. We propose a novel approach that
directly encodes data point neighborhood similarities as a sparse graph. Our
non-iterative framework permits a transparent interpretation of data, without
altering the original data dimension and metric. Several natural and synthetic
data applications demonstrate the efficacy of our novel approach.
Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language
Authors: M Faisal, Sanaullah Manzoor
Date: 2018-02-15 00:00:00
Tasks: Lip Reading, speech-recognition, Speech Recognition

Human lip-reading is a challenging task. It requires not only knowledge of
underlying language but also visual clues to predict spoken words. Experts need
certain level of experience and understanding of visual expressions learning to
decode spoken words. Now-a-days, with the help of deep learning it is possible
to translate lip sequences into meaningful words. The speech recognition in the
noisy environments can be increased with the visual information [1]. To
demonstrate this, in this project, we have tried to train two different
deep-learning models for lip-reading: first one for video sequences using
spatiotemporal convolution neural network, Bi-gated recurrent neural network
and Connectionist Temporal Classification Loss, and second for audio that
inputs the MFCC features to a layer of LSTM cells and output the sequence. We
have also collected a small audio-visual dataset to train and test our model.
Our target is to integrate our both models to improve the speech recognition in
the noisy environment
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Authors: Aline Sindel, Katharina Breininger, Johannes Käßer, Andreas Hess, Andreas Maier, Thomas Köhler
Date: 2018-02-15 00:00:00
Tasks: Image Super-Resolution, regression, Super-Resolution

Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical
structures. However, the acquisition of MR volumes with high spatial resolution
leads to long scan times. To this end, we propose volumetric super-resolution
forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a
locally linear mapping between low-resolution and high-resolution volumetric
image patches by employing random forest regression. We customize features
suitable for volumetric MRI to train the random forest and propose a median
tree ensemble for robust regression. VSRF outperforms state-of-the-art
example-based super-resolution in term of image quality and efficiency for
model training and inference in different MRI datasets. It is also superior to
unsupervised methods with just a handful or even a single volume to assemble
training data.
Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images
Authors: Christoph Wick, Frank Puppe
Date: 2017-11-21 00:00:00
Tasks: Segmentation

We propose a high-performance fully convolutional neural network (FCN) for
historical document segmentation that is designed to process a single page in
one step. The advantage of this model beside its speed is its ability to
directly learn from raw pixels instead of using preprocessing steps e. g.
feature computation or superpixel generation. We show that this network yields
better results than existing methods on different public data sets. For
evaluation of this model we introduce a novel metric that is independent of
ambiguous ground truth called Foreground Pixel Accuracy (FgPA). This pixel
based measure only counts foreground pixels in the binarized page, any
background pixel is omitted. The major advantage of this metric is, that it
enables researchers to compare different segmentation methods on their ability
to successfully segment text or pictures and not on their ability to learn and
possibly overfit the peculiarities of an ambiguous hand-made ground truth
segmentation.
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Authors: Matthieu Lerasle, Zoltan Szabo, Timothee Mathieu, Guillaume Lecue
Date: 2018-02-13 00:00:00

Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mean discrepancy; also called N-distance or energy distance), with numerous successful applications. The representation is constructed as the expectation of the feature map defined by a kernel. As a mean, its classical empirical estimator, however, can be arbitrary severely affected even by a single outlier in case of unbounded features. To the best of our knowledge, unfortunately even the consistency of the existing few techniques trying to alleviate this serious sensitivity bottleneck is unknown. In this paper, we show how the recently emerged principle of median-of-means can be used to design estimators for kernel mean embedding and MMD with excessive resistance properties to outliers, and optimal sub-Gaussian deviation bounds under mild assumptions.
Image Matters: Visually modeling user behaviors using Advanced Model Server
Authors: Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, Pengtao Yi, Sui Huang, Zhiqiang Zhang, Xiaoqiang Zhu, Yu Zhang, Kun Gai
Date: 2017-11-17 00:00:00
Tasks: Click-Through Rate Prediction

In Taobao, the largest e-commerce platform in China, billions of items are
provided and typically displayed with their images. For better user experience
and business effectiveness, Click Through Rate (CTR) prediction in online
advertising system exploits abundant user historical behaviors to identify
whether a user is interested in a candidate ad. Enhancing behavior
representations with user behavior images will help understand user's visual
preference and improve the accuracy of CTR prediction greatly. So we propose to
model user preference jointly with user behavior ID features and behavior
images. However, training with user behavior images brings tens to hundreds of
images in one sample, giving rise to a great challenge in both communication
and computation. To handle these challenges, we propose a novel and efficient
distributed machine learning paradigm called Advanced Model Server (AMS). With
the well known Parameter Server (PS) framework, each server node handles a
separate part of parameters and updates them independently. AMS goes beyond
this and is designed to be capable of learning a unified image descriptor model
shared by all server nodes which embeds large images into low dimensional high
level features before transmitting images to worker nodes. AMS thus
dramatically reduces the communication load and enables the arduous joint
training process. Based on AMS, the methods of effectively combining the images
and ID features are carefully studied, and then we propose a Deep Image CTR
Model. Our approach is shown to achieve significant improvements in both online
and offline evaluations, and has been deployed in Taobao display advertising
system serving the main traffic.
Evolution of Images with Diversity and Constraints Using a Generator Network
Authors: Aneta Neumann, Christo Pyromallis, Bradley Alexander
Date: 2018-02-15 00:00:00
Tasks: Diversity

Evolutionary search has been extensively used to generate artistic images.
Raw images have high dimensionality which makes a direct search for an image
challenging. In previous work this problem has been addressed by using compact
symbolic encodings or by constraining images with priors. Recent developments
in deep learning have enabled a generation of compelling artistic images using
generative networks that encode images with lower-dimensional latent spaces. To
date this work has focused on the generation of images concordant with one or
more classes and transfer of artistic styles. There is currently no work which
uses search in this latent space to generate images scoring high or low
aesthetic measures. In this paper we use evolutionary methods to search for
images in two datasets, faces and butterflies, and demonstrate the effect of
optimising aesthetic feature scores in one or two dimensions. The work gives a
preliminary indication of which feature measures promote the most interesting
images and how some of these measures interact.
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Authors: Raja Giryes, Yonina C. Eldar, Alex M. Bronstein, Guillermo Sapiro
Date: 2016-05-30 00:00:00
Tasks: compressed sensing

Solving inverse problems with iterative algorithms is popular, especially for
large data. Due to time constraints, the number of possible iterations is
usually limited, potentially affecting the achievable accuracy. Given an error
one is willing to tolerate, an important question is whether it is possible to
modify the original iterations to obtain faster convergence to a minimizer
achieving the allowed error without increasing the computational cost of each
iteration considerably. Relying on recent recovery techniques developed for
settings in which the desired signal belongs to some low-dimensional set, we
show that using a coarse estimate of this set may lead to faster convergence at
the cost of an additional reconstruction error related to the accuracy of the
set approximation. Our theory ties to recent advances in sparse recovery,
compressed sensing, and deep learning. Particularly, it may provide a possible
explanation to the successful approximation of the l1-minimization solution by
neural networks with layers representing iterations, as practiced in the
learned iterative shrinkage-thresholding algorithm (LISTA).
Admissible Time Series Motif Discovery with Missing Data
Authors: Yan Zhu, Abdullah Mueen, Eamonn Keogh
Date: 2018-02-15 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The discovery of time series motifs has emerged as one of the most useful
primitives in time series data mining. Researchers have shown its utility for
exploratory data mining, summarization, visualization, segmentation,
classification, clustering, and rule discovery. Although there has been more
than a decade of extensive research, there is still no technique to allow the
discovery of time series motifs in the presence of missing data, despite the
well-documented ubiquity of missing data in scientific, industrial, and medical
datasets. In this work, we introduce a technique for motif discovery in the
presence of missing data. We formally prove that our method is admissible,
producing no false negatives. We also show that our method can piggy-back off
the fastest known motif discovery method with a small constant factor
time/space overhead. We will demonstrate our approach on diverse datasets with
varying amounts of missing data
Tensor-based Nonlinear Classifier for High-Order Data Analysis
Authors: Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos
Date: 2018-02-15 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction

In this paper we propose a tensor-based nonlinear model for high-order data
classification. The advantages of the proposed scheme are that (i) it
significantly reduces the number of weight parameters, and hence of required
training samples, and (ii) it retains the spatial structure of the input
samples. The proposed model, called \textit{Rank}-1 FNN, is based on a
modification of a feedforward neural network (FNN), such that its weights
satisfy the {\it rank}-1 canonical decomposition. We also introduce a new
learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN
on third-order hyperspectral data. Experimental results and comparisons
indicate that the proposed model outperforms state of the art classification
methods, including deep learning based ones, especially in cases with small
numbers of available training samples.