pwc-archive/papers-with-abstracts huggingface dataset:
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Authors: Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin
Date: 2018-08-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image-to-Image Translation, Semantic Segmentation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation techniques are able to map local
texture between two domains, but they are typically unsuccessful when the
domains require larger shape change. Inspired by semantic segmentation, we
introduce a discriminator with dilated convolutions that is able to use
information from across the entire image to train a more context-aware
generator. This is coupled with a multi-scale perceptual loss that is better
able to represent error in the underlying shape of objects. We demonstrate that
this design is more capable of representing shape deformation in a challenging
toy dataset, plus in complex mappings with significant dataset variation
between humans, dolls, and anime faces, and between cats and dogs.
Generating Paths with WFC
Authors: Hugo Scurti, Clark Verbrugge
Date: 2018-08-13 00:00:00
Tasks: Unity

Motion plans are often randomly generated for minor game NPCs. Repetitive or
regular movements, however, require non-trivial programming effort and/or
integration with a pathing system. We here describe an example-based approach
to path generation that requires little or no additional programming effort.
Our work modifies the Wave Function Collapse (WFC) algorithm, adapting it to
produce pathing plans similar to an input sketch. We show how simple sketch
modifications control path characteristics, and demonstrate feasibility through
a usable Unity implementation.
Comparing morphological complexity of Spanish, Otomi and Nahuatl
Authors: Ximena Gutierrez-Vasques, Victor Mijangos
Date: 2018-08-13 00:00:00
Proceeding: WS 2018 8

We use two small parallel corpora for comparing the morphological complexity
of Spanish, Otomi and Nahuatl. These are languages that belong to different
linguistic families, the latter are low-resourced. We take into account two
quantitative criteria, on one hand the distribution of types over tokens in a
corpus, on the other, perplexity and entropy as indicators of word structure
predictability. We show that a language can be complex in terms of how many
different morphological word forms can produce, however, it may be less complex
in terms of predictability of its internal structure of words.
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning
Authors: Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, Wolfgang I. Schöllhorn
Date: 2018-08-13 00:00:00

Machine learning (ML) techniques such as (deep) artificial neural networks
(DNN) are solving very successfully a plethora of tasks and provide new
predictive models for complex physical, chemical, biological and social
systems. However, in most cases this comes with the disadvantage of acting as a
black box, rarely providing information about what made them arrive at a
particular prediction. This black box aspect of ML techniques can be
problematic especially in medical diagnoses, so far hampering a clinical
acceptance. The present paper studies the uniqueness of individual gait
patterns in clinical biomechanics using DNNs. By attributing portions of the
model predictions back to the input variables (ground reaction forces and
full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique
reliably demonstrates which variables at what time windows of the gait cycle
are most relevant for the characterisation of gait patterns from a certain
individual. By measuring the time-resolved contribution of each input variable
to the prediction of ML techniques such as DNNs, our method describes the first
general framework that enables to understand and interpret non-linear ML
methods in (biomechanical) gait analysis and thereby supplies a powerful tool
for analysis, diagnosis and treatment of human gait.
Rank-1 Convolutional Neural Network
Authors: Hyein Kim, Jungho Yoon, Byeongseon Jeong, Sukho Lee
Date: 2018-08-13 00:00:00

In this paper, we propose a convolutional neural network(CNN) with 3-D rank-1
filters which are composed by the outer product of 1-D filters. After being
trained, the 3-D rank-1 filters can be decomposed into 1-D filters in the test
time for fast inference. The reason that we train 3-D rank-1 filters in the
training stage instead of consecutive 1-D filters is that a better gradient
flow can be obtained with this setting, which makes the training possible even
in the case where the network with consecutive 1-D filters cannot be trained.
The 3-D rank-1 filters are updated by both the gradient flow and the outer
product of the 1-D filters in every epoch, where the gradient flow tries to
obtain a solution which minimizes the loss function, while the outer product
operation tries to make the parameters of the filter to live on a rank-1
sub-space. Furthermore, we show that the convolution with the rank-1 filters
results in low rank outputs, constraining the final output of the CNN also to
live on a low dimensional subspace.
Simple Root Cause Analysis by Separable Likelihoods
Authors: Maciej Skorski
Date: 2018-08-13 00:00:00

Root Cause Analysis for Anomalies is challenging because of the trade-off
between the accuracy and its explanatory friendliness, required for industrial
applications. In this paper we propose a framework for simple and friendly RCA
within the Bayesian regime under certain restrictions (that Hessian at the mode
is diagonal, here referred to as \emph{separability}) imposed on the predictive
posterior. We show that this assumption is satisfied for important base models,
including Multinomal, Dirichlet-Multinomial and Naive Bayes. To demonstrate the
usefulness of the framework, we embed it into the Bayesian Net and validate on
web server error logs (real world data set).
Understanding training and generalization in deep learning by Fourier analysis
Authors: Zhi-Qin John Xu
Date: 2018-08-13 00:00:00
Tasks: Deep Learning

Background: It is still an open research area to theoretically understand why
Deep Neural Networks (DNNs)---equipped with many more parameters than training
data and trained by (stochastic) gradient-based methods---often achieve
remarkably low generalization error. Contribution: We study DNN training by
Fourier analysis. Our theoretical framework explains: i) DNN with (stochastic)
gradient-based methods often endows low-frequency components of the target
function with a higher priority during the training; ii) Small initialization
leads to good generalization ability of DNN while preserving the DNN's ability
to fit any function. These results are further confirmed by experiments of DNNs
fitting the following datasets, that is, natural images, one-dimensional
functions and MNIST dataset.
Fast, Better Training Trick --- Random Gradient
Authors: Jiakai Wei
Date: 2018-08-13 00:00:00
Tasks: General Classification, image-classification, Image Classification, Semantic Segmentation

In this paper, we will show an unprecedented method to accelerate training
and improve performance, which called random gradient (RG). This method can be
easier to the training of any model without extra calculation cost, we use
Image classification, Semantic segmentation, and GANs to confirm this method
can improve speed which is training model in computer vision. The central idea
is using the loss multiplied by a random number to random reduce the
back-propagation gradient. We can use this method to produce a better result in
Pascal VOC, Cifar, Cityscapes datasets.
Visual Sensor Network Reconfiguration with Deep Reinforcement Learning
Authors: Paul Jasek, Bernard Abayowa
Date: 2018-08-13 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an approach for reconfiguration of dynamic visual sensor networks
with deep reinforcement learning (RL). Our RL agent uses a modified
asynchronous advantage actor-critic framework and the recently proposed
Relational Network module at the foundation of its network architecture. To
address the issue of sample inefficiency in current approaches to model-free
reinforcement learning, we train our system in an abstract simulation
environment that represents inputs from a dynamic scene. Our system is
validated using inputs from a real-world scenario and preexisting object
detection and tracking algorithms.
Unsupervised Hard Example Mining from Videos for Improved Object Detection
Authors: SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, Erik Learned-Miller
Date: 2018-08-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Detection, object-detection, Object Detection, Pedestrian Detection

Important gains have recently been obtained in object detection by using
training objectives that focus on {\em hard negative} examples, i.e., negative
examples that are currently rated as positive or ambiguous by the detector.
These examples can strongly influence parameters when the network is trained to
correct them. Unfortunately, they are often sparse in the training data, and
are expensive to obtain. In this work, we show how large numbers of hard
negatives can be obtained {\em automatically} by analyzing the output of a
trained detector on video sequences. In particular, detections that are {\em
isolated in time}, i.e., that have no associated preceding or following
detections, are likely to be hard negatives. We describe simple procedures for
mining large numbers of such hard negatives (and also hard {\em positives})
from unlabeled video data. Our experiments show that retraining detectors on
these automatically obtained examples often significantly improves performance.
We present experiments on multiple architectures and multiple data sets,
including face detection, pedestrian detection and other object categories.
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms
Authors: Falco J. Bargagli-Stoffi, Giorgio Gnecco
Date: 2018-08-13 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechanism). The paper contributes to the growing applied machine learning literature on causal inference, by proposing a modified version of the Causal Tree (CT) algorithm to draw causal inference from an irregular assignment mechanism. The proposed method is developed by merging the CT approach with the instrumental variable framework to causal inference, hence the name Causal Tree with Instrumental Variable (CT-IV). As compared to CT, the main strength of CT-IV is that it can deal more efficiently with the heterogeneity of causal effects, as demonstrated by a series of numerical results obtained on synthetic data. Then, the proposed algorithm is used to evaluate a public policy implemented by the Tuscan Regional Administration (Italy), which aimed at easing the access to credit for small firms. In this context, CT-IV breaks fresh ground for target-based policies, identifying interesting heterogeneous causal effects.
BACH: Grand Challenge on Breast Cancer Histology Images
Authors: Guilherme Aresta, Teresa Araújo, Scotty Kwok, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Bahram Marami, Marcel Prastawa, Monica Chan, Michael Donovan, Gerardo Fernandez, Jack Zeineh, Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust, Quoc Dang Vu, Minh Nguyen Nhat To, Eal Kim, Jin Tae Kwak, Sameh Galal, Veronica Sanchez-Freire, Nadia Brancati, Maria Frucci, Daniel Riccio, Yaqi Wang, Lingling Sun, Kaiqiang Ma, Jiannan Fang, Ismael Kone, Lahsen Boulmane, Aurélio Campilho, Catarina Eloy, António Polónia, Paulo Aguiar
Date: 2018-08-13 00:00:00
Tasks: Classification, General Classification, whole slide images

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). A large annotated dataset, composed of both microscopy and whole-slide images, was specifically compiled and made publicly available for the BACH challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publically available as to promote further improvements to the field of automatic classification in digital pathology.
iNNvestigate neural networks!
Authors: Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
Date: 2018-08-13 00:00:00
Tasks: Interpretable Machine Learning

In recent years, deep neural networks have revolutionized many application
domains of machine learning and are key components of many critical decision or
predictive processes. Therefore, it is crucial that domain specialists can
understand and analyze actions and pre- dictions, even of the most complex
neural network architectures. Despite these arguments neural networks are often
treated as black boxes. In the attempt to alleviate this short- coming many
analysis methods were proposed, yet the lack of reference implementations often
makes a systematic comparison between the methods a major effort. The presented
library iNNvestigate addresses this by providing a common interface and
out-of-the- box implementation for many analysis methods, including the
reference implementation for PatternNet and PatternAttribution as well as for
LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an
analysis of image classifications for variety of state-of-the-art neural
network architectures.
Fast Video Shot Transition Localization with Deep Structured Models
Authors: Shitao Tang, Litong Feng, Zhangkui Kuang, Yimin Chen, Wei zhang
Date: 2018-08-13 00:00:00
Tasks: Camera shot boundary detection, GPU

Detection of video shot transition is a crucial pre-processing step in video
analysis. Previous studies are restricted on detecting sudden content changes
between frames through similarity measurement and multi-scale operations are
widely utilized to deal with transitions of various lengths. However,
localization of gradual transitions are still under-explored due to the high
visual similarity between adjacent frames. Cut shot transitions are abrupt
semantic breaks while gradual shot transitions contain low-level
spatial-temporal patterns caused by video effects in addition to the gradual
semantic breaks, e.g. dissolve. In order to address the problem, we propose a
structured network which is able to detect these two shot transitions using
targeted models separately. Considering speed performance trade-offs, we design
a smart framework. With one TITAN GPU, the proposed method can achieve a
30\(\times\) real-time speed. Experiments on public TRECVID07 and RAI databases
show that our method outperforms the state-of-the-art methods. In order to
train a high-performance shot transition detector, we contribute a new database
ClipShots, which contains 128636 cut transitions and 38120 gradual transitions
from 4039 online videos. ClipShots intentionally collect short videos for more
hard cases caused by hand-held camera vibrations, large object motions, and
occlusion.
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
Authors: Apostolos Kemos, Heike Adel, Hinrich Schütze
Date: 2018-08-13 00:00:00
Proceeding: NAACL 2019 6
Tasks: Part-Of-Speech Tagging

Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. But these models still rely on correct token boundaries. In this paper, we propose a novel end-to-end character-level model and demonstrate its effectiveness in multilingual settings and when token boundaries are noisy. Our model is a semi-Markov conditional random field with neural networks for character and segment representation. It requires no tokenizer. The model matches state-of-the-art baselines for various languages and significantly outperforms them on a noisy English version of a part-of-speech tagging benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.
Rapid Adaptation of Neural Machine Translation to New Languages
Authors: Graham Neubig, Junjie Hu
Date: 2018-08-13 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

This paper examines the problem of adapting neural machine translation
systems to new, low-resourced languages (LRLs) as effectively and rapidly as
possible. We propose methods based on starting with massively multilingual
"seed models", which can be trained ahead-of-time, and then continuing training
on data related to the LRL. We contrast a number of strategies, leading to a
novel, simple, yet effective method of "similar-language regularization", where
we jointly train on both a LRL of interest and a similar high-resourced
language to prevent over-fitting to small LRL data. Experiments demonstrate
that massively multilingual models, even without any explicit adaptation, are
surprisingly effective, achieving BLEU scores of up to 15.5 with no data from
the LRL, and that the proposed similar-language regularization method improves
over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.
Code to reproduce experiments at https://github.com/neubig/rapid-adaptation
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks
Authors: Nils Gessert, Matthias Lutz, Markus Heyder, Sarah Latus, David M. Leistner, Youssef S. Abdelwahed, Alexander Schlaefer
Date: 2018-08-13 00:00:00
Tasks: Data Augmentation, Deep Learning, Specificity, Transfer Learning

Coronary heart disease is a common cause of death despite being preventable.
To treat the underlying plaque deposits in the arterial walls, intravascular
optical coherence tomography can be used by experts to detect and characterize
the lesions. In clinical routine, hundreds of images are acquired for each
patient which requires automatic plaque detection for fast and accurate
decision support. So far, automatic approaches rely on classic machine learning
methods and deep learning solutions have rarely been studied. Given the success
of deep learning methods with other imaging modalities, a thorough
understanding of deep learning-based plaque detection for future clinical
decision support systems is required. We address this issue with a new dataset
consisting of in-vivo patient images labeled by three trained experts. Using
this dataset, we employ state-of-the-art deep learning models that directly
learn plaque classification from the images. For improved performance, we study
different transfer learning approaches. Furthermore, we investigate the use of
cartesian and polar image representations and employ data augmentation
techniques tailored to each representation. We fuse both representations in a
multi-path architecture for more effective feature exploitation. Last, we
address the challenge of plaque differentiation in addition to detection.
Overall, we find that our combined model performs best with an accuracy of
91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate
that building a deep learning-based clinical decision support system for plaque
detection is feasible.
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
Authors: Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
Date: 2018-08-13 00:00:00
Proceeding: ECCV 2018 9

The perspective camera and the isometric surface prior have recently gathered
increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the
recent progress, several challenges remain, particularly the computational
complexity and the unknown camera focal length. In this paper we present a
method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the
perspective camera model and the isometric surface prior with unknown focal
length. In the template-based case, we provide a method to estimate four
parameters of the camera intrinsics. For the template-less scenario of NRSfM,
we propose a method to upgrade reconstructions obtained for one focal length to
another based on local rigidity and the so-called Maximum Depth Heuristics
(MDH). On its basis we propose a method to simultaneously recover the focal
length and the non-rigid shapes. We further solve the problem of incorporating
a large number of points and adding more views in MDH-based NRSfM and
efficiently solve them with Second-Order Cone Programming (SOCP). This does not
require any shape initialization and produces results orders of times faster
than many methods. We provide evaluations on standard sequences with
ground-truth and qualitative reconstructions on challenging YouTube videos.
These evaluations show that our method performs better in both speed and
accuracy than the state of the art.
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point
Authors: Liane Guillou, Christian Hardmeier
Date: 2018-08-13 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Translation

We compare the performance of the APT and AutoPRF metrics for pronoun
translation against a manually annotated dataset comprising human judgements as
to the correctness of translations of the PROTEST test suite. Although there is
some correlation with the human judgements, a range of issues limit the
performance of the automated metrics. Instead, we recommend the use of
semi-automatic metrics and test suites in place of fully automatic metrics.
Multi-Task Learning for Sequence Tagging: An Empirical Study
Authors: Soravit Changpinyo, Hexiang Hu, Fei Sha
Date: 2018-08-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Clustering, Multi-Task Learning

We study three general multi-task learning (MTL) approaches on 11 sequence
tagging tasks. Our extensive empirical results show that in about 50% of the
cases, jointly learning all 11 tasks improves upon either independent or
pairwise learning of the tasks. We also show that pairwise MTL can inform us
what tasks can benefit others or what tasks can be benefited if they are
learned jointly. In particular, we identify tasks that can always benefit
others as well as tasks that can always be harmed by others. Interestingly, one
of our MTL approaches yields embeddings of the tasks that reveal the natural
clustering of semantic and syntactic tasks. Our inquiries have opened the doors
to further utilization of MTL in NLP.