pwc-archive/papers-with-abstracts huggingface dataset:
3D Conceptual Design Using Deep Learning
Authors: Zhangsihao Yang, Haoliang Jiang, Zou Lan
Date: 2018-08-05 00:00:00
Tasks: Deep Learning

This article proposes a data-driven methodology to achieve a fast design
support, in order to generate or develop novel designs covering multiple object
categories. This methodology implements two state-of-the-art Variational
Autoencoder dealing with 3D model data. Our methodology constructs a
self-defined loss function. The loss function, containing the outputs of
certain layers in the autoencoder, obtains combination of different latent
features from different 3D model categories.
  Additionally, this article provide detail explanation to utilize the
Princeton ModelNet40 database, a comprehensive clean collection of 3D CAD
models for objects. After convert the original 3D mesh file to voxel and point
cloud data type, we enable to feed our autoencoder with data of the same size
of dimension. The novelty of this work is to leverage the power of deep
learning methods as an efficient latent feature extractor to explore unknown
designing areas. Through this project, we expect the output can show a clear
and smooth interpretation of model from different categories to develop a fast
design support to generate novel shapes. This final report will explore 1) the
theoretical ideas, 2) the progresses to implement Variantional Autoencoder to
attain implicit features from input shapes, 3) the results of output shapes
during training in selected domains of both 3D voxel data and 3D point cloud
data, and 4) our conclusion and future work to achieve the more outstanding
goal.
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
Authors: Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Date: 2018-08-05 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of the left atrial chamber and assessing its morphology, are
essential for improving our understanding of atrial fibrillation, the most
common type of cardiac arrhythmia. Automation of this process in 3D gadolinium
enhanced-MRI (GE-MRI) data is desirable, as manual delineation is
time-consuming, challenging and observer-dependent. Recently, deep
convolutional neural networks (CNNs) have gained tremendous traction and
achieved state-of-the-art results in medical image segmentation. However, it is
difficult to incorporate local and global information without using contracting
(pooling) layers, which in turn reduces segmentation accuracy for smaller
structures. In this paper, we propose a 3D CNN for volumetric segmentation of
the left atrial chamber in LGE-MRI. Our network is based on the well known
U-Net architecture. We employ a 3D fully convolutional network, with dilated
convolutions in the lowest level of the network, and residual connections
between encoder blocks to incorporate local and global knowledge. The results
show that including global context through the use of dilated convolutions,
helps in domain adaptation, and the overall segmentation accuracy is improved
in comparison to a 3D U-Net.
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
Authors: Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, huan zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin
Date: 2018-08-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Attack

When generating adversarial examples to attack deep neural networks (DNNs),
Lp norm of the added perturbation is usually used to measure the similarity
between original image and adversarial example. However, such adversarial
attacks perturbing the raw input spaces may fail to capture structural
information hidden in the input. This work develops a more general attack
model, i.e., the structured attack (StrAttack), which explores group sparsity
in adversarial perturbations by sliding a mask through images aiming for
extracting key spatial structures. An ADMM (alternating direction method of
multipliers)-based framework is proposed that can split the original problem
into a sequence of analytically solvable subproblems and can be generalized to
implement other attacking methods. Strong group sparsity is achieved in
adversarial perturbations even with the same level of Lp norm distortion as the
state-of-the-art attacks. We demonstrate the effectiveness of StrAttack by
extensive experimental results onMNIST, CIFAR-10, and ImageNet. We also show
that StrAttack provides better interpretability (i.e., better correspondence
with discriminative image regions)through adversarial saliency map (Papernot et
al., 2016b) and class activation map(Zhou et al., 2016).
Instantiation
Authors: Abhijeet Gupta, Gemma Boleda, Sebastian Pado
Date: 2018-08-05 00:00:00
Tasks: Word Embeddings

In computational linguistics, a large body of work exists on distributed
modeling of lexical relations, focussing largely on lexical relations such as
hypernymy (scientist -- person) that hold between two categories, as expressed
by common nouns. In contrast, computational linguistics has paid little
attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These
have investigated in detail by the Knowledge Representation and Semantic Web
communities, but generally not with regard to their linguistic properties.
  Our paper closes this gap by investigating and modeling the lexical relation
of instantiation, which holds between an entity-denoting and a
category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We
present a new, principled dataset for the task of instantiation detection as
well as experiments and analyses on this dataset. We obtain the following
results: (a), entities belonging to one category form a region in
distributional space, but the embedding for the category word is typically
located outside this subspace; (b) it is easy to learn to distinguish entities
from categories from distributional evidence, but due to (a), instantiation
proper is much harder to learn when using common nouns as representations of
categories; (c) this problem can be alleviated by using category
representations based on entity rather than category word embeddings.
Combining Graph-based Dependency Features with Convolutional Neural Network for Answer Triggering
Authors: Deepak Gupta, Sarah Kohail, Pushpak Bhattacharyya
Date: 2018-08-05 00:00:00

Answer triggering is the task of selecting the best-suited answer for a given
question from a set of candidate answers if exists. In this paper, we present a
hybrid deep learning model for answer triggering, which combines several
dependency graph based alignment features, namely graph edit distance,
graph-based similarity and dependency graph coverage, with dense vector
embeddings from a Convolutional Neural Network (CNN). Our experiments on the
WikiQA dataset show that such a combination can more accurately trigger a
candidate answer compared to the previous state-of-the-art models. Comparative
study on WikiQA dataset shows 5.86% absolute F-score improvement at the
question level.
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
Authors: Muhammad Yousefnezhad, Daoqiang Zhang
Date: 2018-08-05 00:00:00
Tasks: General Classification

In order to decode the human brain, Multivariate Pattern (MVP) classification
generates cognitive models by using functional Magnetic Resonance Imaging
(fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in
multi-subject fMRI dataset must be mapped to a shared space and then a
classification model is generated by employing the mapped patterns. However,
the MVP models may not provide stable performance on a new fMRI dataset because
the standard pipeline uses disjoint steps for generating these models. Indeed,
each step in the pipeline includes an objective function with independent
optimization approach, where the best solution of each step may not be optimum
for the next steps. For tackling the mentioned issue, this paper introduces the
Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective
function for MVP analysis rather than just using those disjoint steps. For
solving the integrated problem, we proposed a customized multi-objective
optimization approach, where all possible solutions are firstly generated, and
then our method ranks and selects the robust solutions as the final results.
Empirical studies confirm that the proposed method can generate superior
performance in comparison with other techniques.
Self-Attention Recurrent Network for Saliency Detection
Authors: Fengdong Sun, Wenhui Li, Yuanyuan Guan
Date: 2018-08-05 00:00:00
Tasks: Saliency Detection

Feature maps in deep neural network generally contain different semantics.
Existing methods often omit their characteristics that may lead to sub-optimal
results. In this paper, we propose a novel end-to-end deep saliency network
which could effectively utilize multi-scale feature maps according to their
characteristics. Shallow layers often contain more local information, and deep
layers have advantages in global semantics. Therefore, the network generates
elaborate saliency maps by enhancing local and global information of feature
maps in different layers. On one hand, local information of shallow layers is
enhanced by a recurrent structure which shared convolution kernel at different
time steps. On the other hand, global information of deep layers is utilized by
a self-attention module, which generates different attention weights for
salient objects and backgrounds thus achieve better performance. Experimental
results on four widely used datasets demonstrate that our method has advantages
in performance over existing algorithms.
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling
Authors: Zhijian Ou
Date: 2018-08-05 00:00:00

This document aims to provide a review on learning with deep generative
models (DGMs), which is an highly-active area in machine learning and more
generally, artificial intelligence. This review is not meant to be a tutorial,
but when necessary, we provide self-contained derivations for completeness.
This review has two features. First, though there are different perspectives to
classify DGMs, we choose to organize this review from the perspective of
graphical modeling, because the learning methods for directed DGMs and
undirected DGMs are fundamentally different. Second, we differentiate model
definitions from model learning algorithms, since different learning algorithms
can be applied to solve the learning problem on the same model, and an
algorithm can be applied to learn different models. We thus separate model
definition and model learning, with more emphasis on reviewing, differentiating
and connecting different learning algorithms. We also discuss promising future
research directions.
Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models
Authors: Christoph Mayer, Radu Timofte, Grégory Paul
Date: 2018-08-05 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Generating training sets for deep convolutional neural networks (DCNNs) is a
bottleneck for modern real-world applications. This is a demanding task for
applications where annotating training data is costly, such as in semantic
segmentation. In the literature, there is still a gap between the performance
achieved by a network trained on full and on weak annotations. In this paper,
we establish a strategy to measure this gap and to identify the ingredients
necessary to reduce it.
  On scribbles, we establish new state-of-the-art results: we obtain a mIoU of
75.6% without, and 75.7% with CRF post-processing. We reduce the gap by 64.2%
whereas the current state-of-the-art reduces it only by 57.5%. Thanks to a
systematic study of the different ingredients involved in the weakly supervised
scenario and an original experimental strategy, we unravel a counter-intuitive
mechanism that is simple and amenable to generalisations to other
weakly-supervised scenarios: averaging poor local predicted annotations with
the baseline ones and reuse them for training a DCNN yields new
state-of-the-art results.
Multi-Scale Supervised Network for Human Pose Estimation
Authors: Lipeng Ke, Ming-Ching Chang, Honggang Qi, Siwei Lyu
Date: 2018-08-05 00:00:00
Tasks: Activity Recognition, Keypoint Detection, Pose Estimation, regression

Human pose estimation is an important topic in computer vision with many
applications including gesture and activity recognition. However, pose
estimation from image is challenging due to appearance variations, occlusions,
clutter background, and complex activities. To alleviate these problems, we
develop a robust pose estimation method based on the recent deep conv-deconv
modules with two improvements: (1) multi-scale supervision of body keypoints,
and (2) a global regression to improve structural consistency of keypoints. We
refine keypoint detection heatmaps using layer-wise multi-scale supervision to
better capture local contexts. Pose inference via keypoint association is
optimized globally using a regression network at the end. Our method can
effectively disambiguate keypoint matches in close proximity including the
mismatch of left-right body parts, and better infer occluded parts.
Experimental results show that our method achieves competitive performance
among state-of-the-art methods on the MPII and FLIC datasets.
Predicting Learning Status in MOOCs using LSTM
Authors: Zhemin Liu, Feng Xiong, Kaifa Zou, Hongzhi Wang
Date: 2018-08-05 00:00:00
Tasks: General Classification, Prediction, Time Series, Time Series Analysis, Time Series Classification, Time Series Prediction

Real-time and open online course resources of MOOCs have attracted a large
number of learners in recent years. However, many new questions were emerging
about the high dropout rate of learners. For MOOCs platform, predicting the
learning status of MOOCs learners in real time with high accuracy is the
crucial task, and it also help improve the quality of MOOCs teaching. The
prediction task in this paper is inherently a time series prediction problem,
and can be treated as time series classification problem, hence this paper
proposed a prediction model based on RNNLSTMs and optimization techniques which
can be used to predict learners' learning status. Using datasets provided by
Chinese University MOOCs as the inputs of model, the average accuracy of
model's outputs was about 90%.
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
Authors: Rick Salay, Krzysztof Czarnecki
Date: 2018-08-05 00:00:00

The use of machine learning (ML) is on the rise in many sectors of software
development, and automotive software development is no different. In
particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving
Systems (ADS) are two areas where ML plays a significant role. In automotive
development, safety is a critical objective, and the emergence of standards
such as ISO 26262 has helped focus industry practices to address safety in a
systematic and consistent way. Unfortunately, these standards were not designed
to accommodate technologies such as ML or the type of functionality that is
provided by an ADS and this has created a conflict between the need to innovate
and the need to improve safety. In this report, we take steps to address this
conflict by doing a detailed assessment and adaption of ISO 26262 for ML,
specifically in the context of supervised learning. First we analyze the key
factors that are the source of the conflict. Then we assess each software
development process requirement (Part 6 of ISO 26262) for applicability to ML.
Where there are gaps, we propose new requirements to address the gaps. Finally
we discuss the application of this adapted and extended variant of Part 6 to ML
development scenarios.
Classification of Dermoscopy Images using Deep Learning
Authors: Nithin D Reddy
Date: 2018-08-05 00:00:00
Tasks: Classification, Deep Learning, General Classification

Skin cancer is one of the most common forms of cancer and its incidence is
projected to rise over the next decade. Artificial intelligence is a viable
solution to the issue of providing quality care to patients in areas lacking
access to trained dermatologists. Considerable progress has been made in the
use of automated applications for accurate classification of skin lesions from
digital images. In this manuscript, we discuss the design and implementation of
a deep learning algorithm for classification of dermoscopy images from the
HAM10000 Dataset. We trained a convolutional neural network based on the
ResNet50 architecture to accurately classify dermoscopy images of skin lesions
into one of seven disease categories. Using our custom model, we obtained a
balanced accuracy of 91% on the validation dataset.
Learning monocular depth estimation with unsupervised trinocular assumptions
Authors: Matteo Poggi, Fabio Tosi, Stefano Mattoccia
Date: 2018-08-05 00:00:00
Tasks: Depth Estimation, Image Reconstruction, Monocular Depth Estimation, Unsupervised Monocular Depth Estimation

Obtaining accurate depth measurements out of a single image represents a
fascinating solution to 3D sensing. CNNs led to considerable improvements in
this field, and recent trends replaced the need for ground-truth labels with
geometry-guided image reconstruction signals enabling unsupervised training.
Currently, for this purpose, state-of-the-art techniques rely on images
acquired with a binocular stereo rig to predict inverse depth (i.e., disparity)
according to the aforementioned supervision principle. However, these methods
suffer from well-known problems near occlusions, left image border, etc
inherited from the stereo setup. Therefore, in this paper, we tackle these
issues by moving to a trinocular domain for training. Assuming the central
image as the reference, we train a CNN to infer disparity representations
pairing such image with frames on its left and right side. This strategy allows
obtaining depth maps not affected by typical stereo artifacts. Moreover, being
trinocular datasets seldom available, we introduce a novel interleaved training
procedure enabling to enforce the trinocular assumption outlined from current
binocular datasets. Exhaustive experimental results on the KITTI dataset
confirm that our proposal outperforms state-of-the-art methods for unsupervised
monocular depth estimation trained on binocular stereo pairs as well as any
known methods relying on other cues.
Pixel-level Semantics Guided Image Colorization
Authors: Jiaojiao Zhao, Li Liu, Cees G. M. Snoek, Jungong Han, Ling Shao
Date: 2018-08-05 00:00:00
Tasks: Colorization, Image Colorization, Segmentation, Semantic Segmentation

While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from the problems of context confusion and edge color bleeding. To
address context confusion, we propose to incorporate the pixel-level object
semantics to guide the image colorization. The rationale is that human beings
perceive and distinguish colors based on the object's semantic categories. We
propose a hierarchical neural network with two branches. One branch learns what
the object is while the other branch learns the object's colors. The network
jointly optimizes a semantic segmentation loss and a colorization loss. To
attack edge color bleeding we generate more continuous color maps with sharp
edges by adopting a joint bilateral upsamping layer at inference. Our network
is trained on PASCAL VOC2012 and COCO-stuff with semantic segmentation labels
and it produces more realistic and finer results compared to the colorization
state-of-the-art.
Spherical Harmonic Residual Network for Diffusion Signal Harmonization
Authors: Simon Koppers, Luke Bloy, Jeffrey I. Berman, Chantal M. W. Tax, J. Christopher Edgar, Dorit Merhof
Date: 2018-08-05 00:00:00
Tasks: Diffusion  MRI

Diffusion imaging is an important method in the field of neuroscience, as it
is sensitive to changes within the tissue microstructure of the human brain.
However, a major challenge when using MRI to derive quantitative measures is
that the use of different scanners, as used in multi-site group studies,
introduces measurement variability. This can lead to an increased variance in
quantitative metrics, even if the same brain is scanned.
  Contrary to the assumption that these characteristics are comparable and
similar, small changes in these values are observed in many clinical studies,
hence harmonization of the signals is essential.
  In this paper, we present a method that does not require additional
preprocessing, such as segmentation or registration, and harmonizes the signal
based on a deep learning residual network. For this purpose, a training
database is required, which consist of the same subjects, scanned on different
scanners.
  The results show that harmonized signals are significantly more similar to
the ground truth signal compared to no harmonization, but also improve in
comparison to another deep learning method. The same effect is also
demonstrated in commonly used metrics derived from the diffusion MRI signal.
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation
Authors: Pankaj Gupta, Hinrich Schütze
Date: 2018-08-05 00:00:00
Proceeding: WS 2018 11
Tasks: Decision Making, Relation Classification, slot-filling, Slot Filling

Recurrent neural networks (RNNs) are temporal networks and cumulative in
nature that have shown promising results in various natural language processing
tasks. Despite their success, it still remains a challenge to understand their
hidden behavior. In this work, we analyze and interpret the cumulative nature
of RNN via a proposed technique named as Layer-wIse-Semantic-Accumulation
(LISA) for explaining decisions and detecting the most likely (i.e., saliency)
patterns that the network relies on while decision making. We demonstrate (1)
LISA: "How an RNN accumulates or builds semantics during its sequential
processing for a given text example and expected response" (2) Example2pattern:
"How the saliency patterns look like for each category in the data according to
the network in decision making". We analyse the sensitiveness of RNNs about
different inputs to check the increase or decrease in prediction scores and
further extract the saliency patterns learned by the network. We employ two
relation classification datasets: SemEval 10 Task 8 and TAC KBP Slot Filling to
explain RNN predictions via the LISA and example2pattern.