pwc-archive/papers-with-abstracts huggingface dataset:
Augmentor: An Image Augmentation Library for Machine Learning
Authors: Marcus D. Bloice, Christof Stocker, Andreas Holzinger
Date: 2017-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Image Augmentation

The generation of artificial data based on existing observations, known as
data augmentation, is a technique used in machine learning to improve model
accuracy, generalisation, and to control overfitting. Augmentor is a software
package, available in both Python and Julia versions, that provides a high
level API for the expansion of image data using a stochastic, pipeline-based
approach which effectively allows for images to be sampled from a distribution
of augmented images at runtime. Augmentor provides methods for most standard
augmentation practices as well as several advanced features such as
label-preserving, randomised elastic distortions, and provides many helper
functions for typical augmentation tasks used in machine learning.
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
Authors: Jianming Lv, Qing Li, Xintong Wang
Date: 2016-11-23 00:00:00
Tasks: Prediction, Trajectory Prediction

Precise destination prediction of taxi trajectories can benefit many
intelligent location based services such as accurate ad for passengers.
Traditional prediction approaches, which treat trajectories as one-dimensional
sequences and process them in single scale, fail to capture the diverse
two-dimensional patterns of trajectories in different spatial scales. In this
paper, we propose T-CONV which models trajectories as two-dimensional images,
and adopts multi-layer convolutional neural networks to combine multi-scale
trajectory patterns to achieve precise prediction. Furthermore, we conduct
gradient analysis to visualize the multi-scale spatial patterns captured by
T-CONV and extract the areas with distinct influence on the ultimate
prediction. Finally, we integrate multiple local enhancement convolutional
fields to explore these important areas deeply for better prediction.
Comprehensive experiments based on real trajectory data show that T-CONV can
achieve higher accuracy than the state-of-the-art methods.
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams
Authors: Junhong Wang, Shuliang Xu, Bingqian Duan, Caifeng Liu, Jiye Liang
Date: 2017-08-11 00:00:00
Tasks: Classification, Drift Detection, General Classification

Data stream mining problem has caused widely concerns in the area of machine
learning and data mining. In some recent studies, ensemble classification has
been widely used in concept drift detection, however, most of them regard
classification accuracy as a criterion for judging whether concept drift
happening or not. Information entropy is an important and effective method for
measuring uncertainty. Based on the information entropy theory, a new algorithm
using information entropy to evaluate a classification result is developed. It
uses ensemble classification techniques, and the weight of each classifier is
decided through the entropy of the result produced by an ensemble classifiers
system. When the concept in data streams changing, the classifiers' weight
below a threshold value will be abandoned to adapt to a new concept in one
time. In the experimental analysis section, six databases and four proposed
algorithms are executed. The results show that the proposed method can not only
handle concept drift effectively, but also have a better classification
accuracy and time performance than the contrastive algorithms.
Generalized optimal sub-pattern assignment metric
Authors: Abu Sajana Rahmathullah, Ángel F. García-Fernández, Lennart Svensson
Date: 2016-01-21 00:00:00

This paper presents the generalized optimal sub-pattern assignment (GOSPA)
metric on the space of finite sets of targets. Compared to the well-established
optimal sub-pattern assignment (OSPA) metric, GOSPA is unnormalized as a
function of the cardinality and it penalizes cardinality errors differently,
which enables us to express it as an optimisation over assignments instead of
permutations. An important consequence of this is that GOSPA allows us to
penalize localization errors for detected targets and the errors due to missed
and false targets, as indicated by traditional multiple target tracking (MTT)
performance measures, in a sound manner. In addition, we extend the GOSPA
metric to the space of random finite sets, which is important to evaluate MTT
algorithms via simulations in a rigorous way.
Break it Down for Me: A Study in Automated Lyric Annotation
Authors: Lucas Sterckx, Jason Naradowsky, Bill Byrne, Thomas Demeester, Chris Develder
Date: 2017-08-11 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Retrieval, Text Simplification, Translation

Comprehending lyrics, as found in songs and poems, can pose a challenge to
human and machine readers alike. This motivates the need for systems that can
understand the ambiguity and jargon found in such creative texts, and provide
commentary to aid readers in reaching the correct interpretation. We introduce
the task of automated lyric annotation (ALA). Like text simplification, a goal
of ALA is to rephrase the original text in a more easily understandable manner.
However, in ALA the system must often include additional information to clarify
niche terminology and abstract concepts. To stimulate research on this task, we
release a large collection of crowdsourced annotations for song lyrics. We
analyze the performance of translation and retrieval models on this task,
measuring performance with both automated and human evaluation. We find that
each model captures a unique type of information important to the task.
Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices
Authors: Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä
Date: 2016-11-29 00:00:00

Structure from motion algorithms have an inherent limitation that the
reconstruction can only be determined up to the unknown scale factor. Modern
mobile devices are equipped with an inertial measurement unit (IMU), which can
be used for estimating the scale of the reconstruction. We propose a method
that recovers the metric scale given inertial measurements and camera poses. In
the process, we also perform a temporal and spatial alignment of the camera and
the IMU. Therefore, our solution can be easily combined with any existing
visual reconstruction software. The method can cope with noisy camera pose
estimates, typically caused by motion blur or rolling shutter artifacts, via
utilizing a Rauch-Tung-Striebel (RTS) smoother. Furthermore, the scale
estimation is performed in the frequency domain, which provides more robustness
to inaccurate sensor time stamps and noisy IMU samples than the previously used
time domain representation. In contrast to previous methods, our approach has
no parameters that need to be tuned for achieving a good performance. In the
experiments, we show that the algorithm outperforms the state-of-the-art in
both accuracy and convergence speed of the scale estimate. The accuracy of the
scale is around $1\%$ from the ground truth depending on the recording. We also
demonstrate that our method can improve the scale accuracy of the Project
Tango's build-in motion tracking.
Improved Quick Hypervolume Algorithm
Authors: Andrzej Jaszkiewicz
Date: 2016-12-11 00:00:00
Tasks: Evolutionary Algorithms

In this paper, we present a significant improvement of Quick Hypervolume
algorithm, one of the state-of-the-art algorithms for calculating exact
hypervolume of the space dominated by a set of d-dimensional points. This value
is often used as a quality indicator in multiobjective evolutionary algorithms
and other multiobjective metaheuristics and the efficiency of calculating this
indicator is of crucial importance especially in the case of large sets or many
dimensional objective spaces. We use a similar divide and conquer scheme as in
the original Quick Hypervolume algorithm, but in our algorithm we split the
problem into smaller sub-problems in a different way. Through both theoretical
analysis and computational study we show that our approach improves
computational complexity of the algorithm and practical running times.
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data
Authors: Chih-Chung Hsu, Chia-Wen Lin
Date: 2017-05-19 00:00:00
Tasks: Clustering, Representation Learning

Given a large unlabeled set of images, how to efficiently and effectively
group them into clusters based on extracted visual representations remains a
challenging problem. To address this problem, we propose a convolutional neural
network (CNN) to jointly solve clustering and representation learning in an
iterative manner. In the proposed method, given an input image set, we first
randomly pick k samples and extract their features as initial cluster centroids
using the proposed CNN with an initial model pre-trained from the ImageNet
dataset. Mini-batch k-means is then performed to assign cluster labels to
individual input samples for a mini-batch of images randomly sampled from the
input image set until all images are processed. Subsequently, the proposed CNN
simultaneously updates the parameters of the proposed CNN and the centroids of
image clusters iteratively based on stochastic gradient descent. We also
proposed a feature drift compensation scheme to mitigate the drift error caused
by feature mismatch in representation learning. Experimental results
demonstrate the proposed method outperforms start-of-the-art clustering schemes
in terms of accuracy and storage complexity on large-scale image sets
containing millions of images.
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
Authors: Diego Molla-Aliod
Date: 2017-06-07 00:00:00
Tasks: regression

Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
focused on the use of query-based extractive summarisation techniques for the
generation of the ideal answers. Four runs were submitted, with approaches
ranging from a trivial system that selected the first $n$ snippets, to the use
of deep learning approaches under a regression framework. Our experiments and
the ROUGE results of the five test batches of BioASQ indicate surprisingly good
results for the trivial approach. Overall, most of our runs on the first three
test batches achieved the best ROUGE-SU4 results in the challenge.
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Authors: Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim
Date: 2016-12-21 00:00:00
Tasks: Mitosis Detection, whole slide images

We present a unified framework to predict tumor proliferation scores from
breast histopathology whole slide images. Our system offers a fully automated
solution to predicting both a molecular data-based, and a mitosis
counting-based tumor proliferation score. The framework integrates three
modules, each fine-tuned to maximize the overall performance: An image
processing component for handling whole slide images, a deep learning based
mitosis detection network, and a proliferation scores prediction module. We
have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based
score prediction and 0.652 F1-score in mitosis detection. On Spearman's
correlation coefficient, which evaluates predictive accuracy on the molecular
data based score, the system obtained 0.6171. Our approach won first place in
all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which
is MICCAI grand challenge.
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Authors: Yuanzhouhan Cao, Zifeng Wu, Chunhua Shen
Date: 2016-05-08 00:00:00
Tasks: Depth Estimation, General Classification, Scene Understanding

Depth estimation from single monocular images is a key component of scene
understanding and has benefited largely from deep convolutional neural networks
(CNN) recently. In this article, we take advantage of the recent deep residual
networks and propose a simple yet effective approach to this problem. We
formulate depth estimation as a pixel-wise classification task. Specifically,
we first discretize the continuous depth values into multiple bins and label
the bins according to their depth range. Then we train fully convolutional deep
residual networks to predict the depth label of each pixel. Performing discrete
depth label classification instead of continuous depth value regression allows
us to predict a confidence in the form of probability distribution. We further
apply fully-connected conditional random fields (CRF) as a post processing step
to enforce local smoothness interactions, which improves the results. We
evaluate our approach on both indoor and outdoor datasets and achieve
state-of-the-art performance.
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition
Authors: Sunil Kumar Sahu, Ashish Anand
Date: 2017-08-11 00:00:00
Tasks: Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, TAG

Most existing methods for biomedical entity recognition task rely on explicit
feature engineering where many features either are specific to a particular
task or depends on output of other existing NLP tools. Neural architectures
have been shown across various domains that efforts for explicit feature design
can be reduced. In this work we propose an unified framework using
bi-directional long short term memory network (BLSTM) for named entity
recognition (NER) tasks in biomedical and clinical domains. Three important
characteristics of the framework are as follows - (1) model learns contextual
as well as morphological features using two different BLSTM in hierarchy, (2)
model uses first order linear conditional random field (CRF) in its output
layer in cascade of BLSTM to infer label or tag sequence, (3) model does not
use any domain specific features or dictionary, i.e., in another words, same
set of features are used in the three NER tasks, namely, disease name
recognition (Disease NER), drug name recognition (Drug NER) and clinical entity
recognition (Clinical NER). We compare performance of the proposed model with
existing state-of-the-art models on the standard benchmark datasets of the
three tasks. We show empirically that the proposed framework outperforms all
existing models. Further our analysis of CRF layer and word-embedding obtained
using character based embedding show their importance.
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
Authors: Masanori Suganuma, Shinichi Shirakawa, Tomoharu Nagao
Date: 2017-04-03 00:00:00
Tasks: General Classification, image-classification, Image Classification, Neural Architecture Search

The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.
Variational Deep Semantic Hashing for Text Documents
Authors: Suthee Chaidaroon, Yi Fang
Date: 2017-08-11 00:00:00
Tasks: Image Retrieval, Information Retrieval, Retrieval, Supervised Text Retrieval, Text Retrieval, Variational Inference

As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
  In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.
Towards Diverse and Natural Image Descriptions via a Conditional GAN
Authors: Bo Dai, Sanja Fidler, Raquel Urtasun, Dahua Lin
Date: 2017-03-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Captioning, Reinforcement Learning

Despite the substantial progress in recent years, the image captioning
techniques are still far from being perfect.Sentences produced by existing
methods, e.g. those based on RNNs, are often overly rigid and lacking in
variability. This issue is related to a learning principle widely used in
practice, that is, to maximize the likelihood of training samples. This
principle encourages high resemblance to the "ground-truth" captions while
suppressing other reasonable descriptions. Conventional evaluation metrics,
e.g. BLEU and METEOR, also favor such restrictive methods. In this paper, we
explore an alternative approach, with the aim to improve the naturalness and
diversity -- two essential properties of human expression. Specifically, we
propose a new framework based on Conditional Generative Adversarial Networks
(CGAN), which jointly learns a generator to produce descriptions conditioned on
images and an evaluator to assess how well a description fits the visual
content. It is noteworthy that training a sequence generator is nontrivial. We
overcome the difficulty by Policy Gradient, a strategy stemming from
Reinforcement Learning, which allows the generator to receive early feedback
along the way. We tested our method on two large datasets, where it performed
competitively against real people in our user study and outperformed other
methods on various tasks.
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
Authors: Jung Uk Kim, Hak Gu Kim, Yong Man Ro
Date: 2017-08-11 00:00:00
Tasks: Decoder, Deep Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a novel medical image segmentation using iterative
deep learning framework. We have combined an iterative learning approach and an
encoder-decoder network to improve segmentation results, which enables to
precisely localize the regions of interest (ROIs) including complex shapes or
detailed textures of medical images in an iterative manner. The proposed
iterative deep convolutional encoder-decoder network consists of two main
paths: convolutional encoder path and convolutional decoder path with iterative
learning. Experimental results show that the proposed iterative deep learning
framework is able to yield excellent medical image segmentation performances
for various medical images. The effectiveness of the proposed method has been
proved by comparing with other state-of-the-art medical image segmentation
methods.
Divide and Fuse: A Re-ranking Approach for Person Re-identification
Authors: Rui Yu, Zhichao Zhou, Song Bai, Xiang Bai
Date: 2017-08-11 00:00:00
Tasks: Diversity, Person Re-Identification, Re-Ranking

As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.
Learning from Noisy Label Distributions
Authors: Yuya Yoshikawa
Date: 2017-08-11 00:00:00

In this paper, we consider a novel machine learning problem, that is,
learning a classifier from noisy label distributions. In this problem, each
instance with a feature vector belongs to at least one group. Then, instead of
the true label of each instance, we observe the label distribution of the
instances associated with a group, where the label distribution is distorted by
an unknown noise. Our goals are to (1) estimate the true label of each
instance, and (2) learn a classifier that predicts the true label of a new
instance. We propose a probabilistic model that considers true label
distributions of groups and parameters that represent the noise as hidden
variables. The model can be learned based on a variational Bayesian method. In
numerical experiments, we show that the proposed model outperforms existing
methods in terms of the estimation of the true labels of instances.
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
Authors: Wenqi Ren, Jinshan Pan, Xiaochun Cao, Ming-Hsuan Yang
Date: 2017-08-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Optical Flow Estimation, Semantic Segmentation, Video Deblurring

Video deblurring is a challenging problem as the blur is complex and usually
caused by the combination of camera shakes, object motions, and depth
variations. Optical flow can be used for kernel estimation since it predicts
motion trajectories. However, the estimates are often inaccurate in complex
scenes at object boundaries, which are crucial in kernel estimation. In this
paper, we exploit semantic segmentation in each blurry frame to understand the
scene contents and use different motion models for image regions to guide
optical flow estimation. While existing pixel-wise blur models assume that the
blur kernel is the same as optical flow during the exposure time, this
assumption does not hold when the motion blur trajectory at a pixel is
different from the estimated linear optical flow. We analyze the relationship
between motion blur trajectory and optical flow, and present a novel pixel-wise
non-linear kernel model to account for motion blur. The proposed blur model is
based on the non-linear optical flow, which describes complex motion blur more
effectively. Extensive experiments on challenging blurry videos demonstrate the
proposed algorithm performs favorably against the state-of-the-art methods.