pwc-archive/papers-with-abstracts huggingface dataset:
Privacy-Preserving Distributed Deep Learning for Clinical Data
Authors: Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu
Date: 2018-12-04 00:00:00
Tasks: Deep Learning, Privacy Preserving

Deep learning with medical data often requires larger samples sizes than are
available at single providers. While data sharing among institutions is
desirable to train more accurate and sophisticated models, it can lead to
severe privacy concerns due the sensitive nature of the data. This problem has
motivated a number of studies on distributed training of neural networks that
do not require direct sharing of the training data. However, simple distributed
training does not offer provable privacy guarantees to satisfy technical safe
standards and may reveal information about the underlying patients. We present
a method to train neural networks for clinical data in a distributed fashion
under differential privacy. We demonstrate these methods on two datasets that
include information from multiple independent sites, the eICU collaborative
Research Database and The Cancer Genome Atlas.
CompILE: Compositional Imitation Learning and Execution
Authors: Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia
Date: 2018-12-04 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning

We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data. CompILE uses a novel unsupervised, fully-differentiable sequence segmentation module to learn latent encodings of sequential data that can be re-composed and executed to perform new tasks. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate CompILE in a challenging 2D multi-task environment and a continuous control task, and show that it can find correct task boundaries and event encodings in an unsupervised manner. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our CompILE-based agent could learn given only sparse rewards, where agents without task-specific policies struggle.
Matrix Factorization via Deep Learning
Authors: Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Date: 2018-12-04 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Matrix Completion

Matrix completion is one of the key problems in signal processing and machine
learning. In recent years, deep-learning-based models have achieved
state-of-the-art results in matrix completion. Nevertheless, they suffer from
two drawbacks: (i) they can not be extended easily to rows or columns unseen
during training; and (ii) their results are often degraded in case discrete
predictions are required. This paper addresses these two drawbacks by
presenting a deep matrix factorization model and a generic method to allow
joint training of the factorization model and the discretization operator.
Experiments on a real movie rating dataset show the efficacy of the proposed
models.
Cross-spectral Periocular Recognition: A Survey
Authors: S. S. Behera, Bappaditya Mandal, N. B. Puhan
Date: 2018-12-04 00:00:00
Tasks: Survey

Among many biometrics such as face, iris, fingerprint and others, periocular
region has the advantages over other biometrics because it is non-intrusive and
serves as a balance between iris or eye region (very stringent, small area) and
the whole face region (very relaxed large area). Research have shown that this
is the region which does not get affected much because of various poses, aging,
expression, facial changes and other artifacts, which otherwise would change to
a large variation. Active research has been carried out on this topic since
past few years due to its obvious advantages over face and iris biometrics in
unconstrained and uncooperative scenarios. Many researchers have explored
periocular biometrics involving both visible (VIS) and infra-red (IR) spectrum
images. For a system to work for 24/7 (such as in surveillance scenarios), the
registration process may depend on the day time VIS periocular images (or any
mug shot image) and the testing or recognition process may occur in the night
time involving only IR periocular images. This gives rise to a challenging
research problem called the cross-spectral matching of images where VIS images
are used for registration or as gallery images and IR images are used for
testing or recognition process and vice versa. After intensive research of more
than two decades on face and iris biometrics in cross-spectral domain, a number
of researchers have now focused their work on matching heterogeneous
(cross-spectral) periocular images. Though a number of surveys have been made
on existing periocular biometric research, no study has been done on its
cross-spectral aspect. This paper analyses and reviews current state-of-the-art
techniques in cross-spectral periocular recognition including various
methodologies, databases, their protocols and current-state-of-the-art
recognition performances.
Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
Authors: Nils Gessert, Lukas Wittig, Daniel Drömann, Tobias Keck, Alexander Schlaefer, David B. Ellebrecht
Date: 2018-12-04 00:00:00
Tasks: Cancer Classification, Colon Cancer Detection In Confocal Laser Microscopy Images, General Classification, Transfer Learning

Histological evaluation of tissue samples is a typical approach to identify
colorectal cancer metastases in the peritoneum. For immediate assessment,
reliable and real-time in-vivo imaging would be required. For example,
intraoperative confocal laser microscopy has been shown to be suitable for
distinguishing organs and also malignant and benign tissue. So far, the
analysis is done by human experts. We investigate the feasibility of automatic
colon cancer classification from confocal laser microscopy images using deep
learning models. We overcome very small dataset sizes through transfer learning
with state-of-the-art architectures. We achieve an accuracy of 89.1% for cancer
detection in the peritoneum which indicates viability as an intraoperative
decision support system.
The Visual Centrifuge: Model-Free Layered Video Representations
Authors: Jean-Baptiste Alayrac, João Carreira, Andrew Zisserman
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Color Constancy, model, Video Understanding

True video understanding requires making sense of non-lambertian scenes where
the color of light arriving at the camera sensor encodes information about not
just the last object it collided with, but about multiple mediums -- colored
windows, dirty mirrors, smoke or rain. Layered video representations have the
potential of accurately modelling realistic scenes but have so far required
stringent assumptions on motion, lighting and shape. Here we propose a
learning-based approach for multi-layered video representation: we introduce
novel uncertainty-capturing 3D convolutional architectures and train them to
separate blended videos. We show that these models then generalize to single
videos, where they exhibit interesting abilities: color constancy, factoring
out shadows and separating reflections. We present quantitative and qualitative
results on real world videos.
Compressive Classification (Machine Learning without learning)
Authors: Vincent Schellekens, Laurent Jacques
Date: 2018-12-04 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Compressive learning is a framework where (so far unsupervised) learning
tasks use not the entire dataset but a compressed summary (sketch) of it. We
propose a compressive learning classification method, and a novel sketch
function for images.
Inferring Point Clouds from Single Monocular Images by Depth Intermediation
Authors: Wei Zeng, Sezer Karaoglu, Theo Gevers
Date: 2018-12-04 00:00:00
Tasks: 3D Object Reconstruction, Depth Estimation, Object, Object Reconstruction, Point Cloud Completion

In this paper, we propose a pipeline to generate 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point cloud completion from partial point clouds. Our method sequentially predicts the depth maps from images and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms existing state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method.
Meta Learning Deep Visual Words for Fast Video Object Segmentation
Authors: Harkirat Singh Behl, Mohammad Najafi, Anurag Arnab, Philip H. S. Torr
Date: 2018-12-04 00:00:00
Tasks: Meta-Learning, Object, Optical Flow Estimation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Personal robots and driverless cars need to be able to operate in novel environments and thus quickly and efficiently learn to recognise new object classes. We address this problem by considering the task of video object segmentation. Previous accurate methods for this task finetune a model using the first annotated frame, and/or use additional inputs such as optical flow and complex post-processing. In contrast, we develop a fast, causal algorithm that requires no finetuning, auxiliary inputs or post-processing, and segments a variable number of objects in a single forward-pass. We represent an object with clusters, or "visual words", in the embedding space, which correspond to object parts in the image space. This allows us to robustly match to the reference objects throughout the video, because although the global appearance of an object changes as it undergoes occlusions and deformations, the appearance of more local parts may stay consistent. We learn these visual words in an unsupervised manner, using meta-learning to ensure that our training objective matches our inference procedure. We achieve comparable accuracy to finetuning based methods (whilst being 1 to 2 orders of magnitude faster), and state-of-the-art in terms of speed/accuracy trade-offs on four video segmentation datasets. Code is available at https://github.com/harkiratbehl/MetaVOS.
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
Authors: Yongchao Xu, Yukang Wang, Wei Zhou, Yongpan Wang, Zhibo Yang, Xiang Bai
Date: 2018-12-04 00:00:00
Tasks: Scene Text Detection, Text Detection

Scene text detection is an important step of scene text reading system. The main challenges lie on significantly varied sizes and aspect ratios, arbitrary orientations and shapes. Driven by recent progress in deep learning, impressive performances have been achieved for multi-oriented text detection. Yet, the performance drops dramatically in detecting curved texts due to the limited text representation (e.g., horizontal bounding boxes, rotated rectangles, or quadrilaterals). It is of great interest to detect curved texts, which are actually very common in natural scenes. In this paper, we present a novel text detector named TextField for detecting irregular scene texts. Specifically, we learn a direction field pointing away from the nearest text boundary to each text point. This direction field is represented by an image of two-dimensional vectors and learned via a fully convolutional neural network. It encodes both binary text mask and direction information used to separate adjacent text instances, which is challenging for classical segmentation-based approaches. Based on the learned direction field, we apply a simple yet effective morphological-based post-processing to achieve the final detection. Experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and CTW1500, respectively, and also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500. Furthermore, TextField is robust in generalizing to unseen datasets. The code is available at https://github.com/YukangWang/TextField.
Semi-Supervised Cross-Modal Retrieval with Label Prediction
Authors: Devraj Mandal, Pramod Rao, Soma Biswas
Date: 2018-12-04 00:00:00
Tasks: Cross-Modal Retrieval, Prediction, Retrieval

Due to abundance of data from multiple modalities, cross-modal retrieval tasks with image-text, audio-image, etc. are gaining increasing importance. Of the different approaches proposed, supervised methods usually give significant improvement over their unsupervised counterparts at the additional cost of labeling or annotation of the training data. Semi-supervised methods are recently becoming popular as they provide an elegant framework to balance the conflicting requirement of labeling cost and accuracy. In this work, we propose a novel deep semi-supervised framework which can seamlessly handle both labeled as well as unlabeled data. The network has two important components: (a) the label prediction component predicts the labels for the unlabeled portion of the data and then (b) a common modality-invariant representation is learned for cross-modal retrieval. The two parts of the network are trained sequentially one after the other. Extensive experiments on three standard benchmark datasets, Wiki, Pascal VOC and NUS-WIDE demonstrate that the proposed framework outperforms the state-of-the-art for both supervised and semi-supervised settings.
A multi-class structured dictionary learning method using discriminant atom selection
Authors: R. E. Rolón, L. E. Di Persia, R. D. Spies, H. L. Rufiner
Date: 2018-12-04 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, Handwritten Digit Recognition, Multi-class Classification

In the last decade, traditional dictionary learning methods have been
successfully applied to various pattern classification tasks. Although these
methods produce sparse representations of signals which are robust against
distortions and missing data, such representations quite often turn out to be
unsuitable if the final objective is signal classification. In order to
overcome or at least to attenuate such a weakness, several new methods which
incorporate discriminative information into sparse-inducing models have emerged
in recent years. In particular, methods for discriminative dictionary learning
have shown to be more accurate (in terms of signal classification) than the
traditional ones, which are only focused on minimizing the total representation
error. In this work, we present both a novel multi-class discriminative measure
and an innovative dictionary learning method. For a given dictionary, this new
measure, which takes into account not only when a particular atom is used for
representing signals coming from a certain class and the magnitude of its
corresponding representation coefficient, but also the effect that such an atom
has in the total representation error, is capable of efficiently quantifying
the degree of discriminability of each one of the atoms. On the other hand, the
new dictionary construction method yields dictionaries which are highly
suitable for multi-class classification tasks. Our method was tested with a
widely used database for handwritten digit recognition and compared with three
state-of-the-art classification methods. The results show that our method
significantly outperforms the other three achieving good recognition rates and
additionally, reducing the computational cost of the classifier.
Bad practices in evaluation methodology relevant to class-imbalanced problems
Authors: Jan Brabec, Lukas Machlica
Date: 2018-12-04 00:00:00

For research to go in the right direction, it is essential to be able to
compare and quantify performance of different algorithms focused on the same
problem. Choosing a suitable evaluation metric requires deep understanding of
the pursued task along with all of its characteristics. We argue that in the
case of applied machine learning, proper evaluation metric is the basic
building block that should be in the spotlight and put under thorough
examination. Here, we address tasks with class imbalance, in which the class of
interest is the one with much lower number of samples. We encountered
non-insignificant amount of recent papers, in which improper evaluation methods
are used, borrowed mainly from the field of balanced problems. Such bad
practices may heavily bias the results in favour of inappropriate algorithms
and give false expectations of the state of the field.
Estimating 6D Pose From Localizing Designated Surface Keypoints
Authors: Zelin Zhao, Gao Peng, Haoyu Wang, Hao-Shu Fang, Chengkun Li, Cewu Lu
Date: 2018-12-04 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation, Pose Prediction

In this paper, we present an accurate yet effective solution for 6D pose
estimation from an RGB image. The core of our approach is that we first
designate a set of surface points on target object model as keypoints and then
train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can
recover the 6D pose according to the 2D-3D relationship of keypoints. Different
from recent state-of-the-art CNN-based approaches that rely on a time-consuming
post-processing procedure, our method can achieve competitive accuracy without
any refinement after pose prediction. Meanwhile, we obtain a 30% relative
improvement in terms of ADD accuracy among methods without using refinement.
Moreover, we succeed in handling heavy occlusion by selecting the most
confident keypoints to recover the 6D pose. For the sake of reproducibility, we
will make our code and models publicly available soon.
Design and implementation of smart cooking based on amazon echo
Authors: Lin Xiaoguang, Yang Yong, Zhang Ju
Date: 2018-12-04 00:00:00
Tasks: Cloud Computing

Smart cooking based on Amazon Echo uses the internet of things and cloud
computing to assist in cooking food. People may speak to Amazon Echo during the
cooking in order to get the information and situation of the cooking. Amazon
Echo recognizes what people say, then transfers the information to the cloud
services, and speaks to people the results that cloud services make by querying
the embedded cooking knowledge and achieving the information of intelligent
kitchen devices online. An intelligent food thermometer and its mobile
application are well-designed and implemented to monitor the temperature of
cooking food.
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
Authors: Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-12-04 00:00:00
Tasks: Instrument Recognition, Object Detection, Surgical tool detection, Video Object Tracking, Weakly-Supervised Object Localization

Purpose: Real-time surgical tool tracking is a core component of the future
intelligent operating room (OR), because it is highly instrumental to analyze
and understand the surgical activities. Current methods for surgical tool
tracking in videos need to be trained on data in which the spatial positions of
the tools are manually annotated. Generating such training data is difficult
and time-consuming. Instead, we propose to use solely binary presence
annotations to train a tool tracker for laparoscopic videos. Methods: The
proposed approach is composed of a CNN + Convolutional LSTM (ConvLSTM) neural
network trained end-to-end, but weakly supervised on tool binary presence
labels only. We use the ConvLSTM to model the temporal dependencies in the
motion of the surgical tools and leverage its spatio-temporal ability to smooth
the class peak activations in the localization heat maps (Lh-maps).
  Results: We build a baseline tracker on top of the CNN model and demonstrate
that our approach based on the ConvLSTM outperforms the baseline in tool
presence detection, spatial localization, and motion tracking by over 5.0%,
13.9%, and 12.6%, respectively.
  Conclusions: In this paper, we demonstrate that binary presence labels are
sufficient for training a deep learning tracking model using our proposed
method. We also show that the ConvLSTM can leverage the spatio-temporal
coherence of consecutive image frames across a surgical video to improve tool
presence detection, spatial localization, and motion tracking.
  keywords: Surgical workflow analysis, tool tracking, weak supervision,
spatio-temporal coherence, ConvLSTM, endoscopic videos
Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks
Authors: Paweł Gora, Maciej Brzeski, Marcin Możejko, Arkadiusz Klemenko, Adrian Kochański
Date: 2018-12-02 00:00:00

We analyze the accuracy of traffic simulations metamodels based on neural
networks and gradient boosting models (LightGBM), applied to traffic
optimization as fitness functions of genetic algorithms. Our metamodels
approximate outcomes of traffic simulations (the total time of waiting on a red
signal) taking as an input different traffic signal settings, in order to
efficiently find (sub)optimal settings. Their accuracy was proven to be very
good on randomly selected test sets, but it turned out that the accuracy may
drop in case of settings expected (according to genetic algorithms) to be close
to local optima, which makes the traffic optimization process more difficult.
In this work, we investigate 16 different metamodels and 20 settings of genetic
algorithms, in order to understand what are the reasons of this phenomenon,
what is its scale, how it can be mitigated and what can be potentially done to
design better real-time traffic optimization methods.
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
Authors: Chenglei Niu, Guojing Zhong, Ying Liu, Yandong Zhang, Yongsheng Sun, Ailong He, Zhaoji Chen
Date: 2018-12-04 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

With the rapid development of online advertising and recommendation systems,
click-through rate prediction is expected to play an increasingly important
role.Recently many DNN-based models which follow a similar Embedding&MLP
paradigm have been proposed, and have achieved good result in image/voice and
nlp fields. In these methods the Wide&Deep model announced by Google plays a
key role.Most models first map large scale sparse input features into
low-dimensional vectors which are transformed to fixed-length vectors, then
concatenated together before being fed into a multilayer perceptron (MLP) to
learn non-linear relations among input features. The number of trainable
variables normally grow dramatically the number of feature fields and the
embedding dimension grow. It is a big challenge to get state-of-the-art result
through training deep neural network and embedding together, which falls into
local optimal or overfitting easily. In this paper, we propose an Structured
Semantic Model (SSM) to tackles this challenge by designing a orthogonal base
convolution and pooling model which adaptively learn the multi-scale base
semantic representation between features supervised by the click label.The
output of SSM are then used in the Wide&Deep for CTR prediction.Experiments on
two public datasets as well as real Weibo production dataset with over 1
billion samples have demonstrated the effectiveness of our proposed approach
with superior performance comparing to state-of-the-art methods.