pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
Authors: Andrea Pilzer, Dan Xu, Mihai Marian Puscas, Elisa Ricci, Nicu Sebe
Date: 2018-07-28 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.
Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification
Authors: T M Feroz Ali, Subhasis Chaudhuri
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Metric Learning, Person Re-Identification

In this paper we propose a novel metric learning framework called Nullspace
Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the
small sample size (SSS) problem inherent in person re-identification and offers
a significant performance gain over existing state-of-the-art methods. Taking
advantage of the very high dimensionality of the feature space, the metric is
learned using a maximum margin criterion (MMC) over a discriminative nullspace
where all training sample points of a given class map onto a single point,
minimizing the within class scatter. A kernel version of MMC is used to obtain
a better between class separation. Extensive experiments on four challenging
benchmark datasets for person re-identification demonstrate that the proposed
algorithm outperforms all existing methods. We obtain 99.8% rank-1 accuracy on
the most widely accepted and challenging dataset VIPeR, compared to the
previous state of the art being only 63.92%.
Back-Translation-Style Data Augmentation for End-to-End ASR
Authors: Tomoki Hayashi, Shinji Watanabe, Yu Zhang, Tomoki Toda, Takaaki Hori, Ramon Astudillo, Kazuya Takeda
Date: 2018-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Decoder, Machine Translation, speech-recognition, Speech Recognition, Translation

In this paper we propose a novel data augmentation method for attention-based
end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of
text which is not paired with speech signals. Inspired by the back-translation
technique proposed in the field of machine translation, we build a neural
text-to-encoder model which predicts a sequence of hidden states extracted by a
pre-trained E2E-ASR encoder from a sequence of characters. By using hidden
states as a target instead of acoustic features, it is possible to achieve
faster attention learning and reduce computational cost, thanks to sub-sampling
in E2E-ASR encoder, also the use of the hidden states can avoid to model
speaker dependencies unlike acoustic features. After training, the
text-to-encoder model generates the hidden states from a large amount of
unpaired text, then E2E-ASR decoder is retrained using the generated hidden
states as additional training data. Experimental evaluation using LibriSpeech
dataset demonstrates that our proposed method achieves improvement of ASR
performance and reduces the number of unknown words without the need for paired
data.
Pairwise Body-Part Attention for Recognizing Human-Object Interactions
Authors: Hao-Shu Fang, Jinkun Cao, Yu-Wing Tai, Cewu Lu
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: feature selection, Human-Object Interaction Detection, Object

In human-object interactions (HOI) recognition, conventional methods consider
the human body as a whole and pay a uniform attention to the entire body
region. They ignore the fact that normally, human interacts with an object by
using some parts of the body. In this paper, we argue that different body parts
should be paid with different attention in HOI recognition, and the
correlations between different body parts should be further considered. This is
because our body parts always work collaboratively. We propose a new pairwise
body-part attention model which can learn to focus on crucial parts, and their
correlations for HOI recognition. A novel attention based feature selection
method and a feature representation scheme that can capture pairwise
correlations between body parts are introduced in the model. Our proposed
approach achieved 4% improvement over the state-of-the-art results in HOI
recognition on the HICO dataset. We will make our model and source codes
publicly available.
Transportation Modes Classification Using Feature Engineering
Authors: Mohammad Etemad
Date: 2018-07-28 00:00:00
Tasks: Classification, Feature Engineering, General Classification, Information Retrieval, Management, Retrieval, Transportation Mode Detection

Predicting transportation modes from GPS (Global Positioning System) records
is a hot topic in the trajectory mining domain. Each GPS record is called a
trajectory point and a trajectory is a sequence of these points. Trajectory
mining has applications including but not limited to transportation mode
detection, tourism, traffic congestion, smart cities management, animal
behaviour analysis, environmental preservation, and traffic dynamics are some
of the trajectory mining applications. Transportation modes prediction as one
of the tasks in human mobility and vehicle mobility applications plays an
important role in resource allocation, traffic management systems, tourism
planning and accident detection. In this work, the proposed framework in Etemad
et al. is extended to consider other aspects in the task of transportation
modes prediction. Wrapper search and information retrieval methods were
investigated to find the best subset of trajectory features. Finding the best
classifier and the best feature subset, the framework is compared against two
related papers that applied deep learning methods. The results show that our
framework achieved better performance. Moreover, the ground truth noise removal
improved accuracy of transportation modes prediction task; however, the
assumption of having access to test set labels in pre-processing task is
invalid. Furthermore, the cross validation approaches were investigated and the
performance results show that the random cross validation method provides
optimistic results.
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Authors: Augustus Odena, Ian Goodfellow
Date: 2018-07-28 00:00:00
Tasks: software testing

Machine learning models are notoriously difficult to interpret and debug.
This is particularly true of neural networks. In this work, we introduce
automated software testing techniques for neural networks that are well-suited
to discovering errors which occur only for rare inputs. Specifically, we
develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,
random mutations of inputs to a neural network are guided by a coverage metric
toward the goal of satisfying user-specified constraints. We describe how fast
approximate nearest neighbor algorithms can provide this coverage metric. We
then discuss the application of CGF to the following goals: finding numerical
errors in trained neural networks, generating disagreements between neural
networks and quantized versions of those networks, and surfacing undesirable
behavior in character level language models. Finally, we release an open source
library called TensorFuzz that implements the described techniques.
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition
Authors: Shubham Toshniwal, Anjuli Kannan, Chung-Cheng Chiu, Yonghui Wu, Tara N. Sainath, Karen Livescu
Date: 2018-07-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Attention-based recurrent neural encoder-decoder models present an elegant
solution to the automatic speech recognition problem. This approach folds the
acoustic model, pronunciation model, and language model into a single network
and requires only a parallel corpus of speech and text for training. However,
unlike in conventional approaches that combine separate acoustic and language
models, it is not clear how to use additional (unpaired) text. While there has
been previous work on methods addressing this problem, a thorough comparison
among methods is still lacking. In this paper, we compare a suite of past
methods and some of our own proposed methods for using unpaired text data to
improve encoder-decoder models. For evaluation, we use the medium-sized
Switchboard data set and the large-scale Google voice search and dictation data
sets. Our results confirm the benefits of using unpaired text across a range of
methods and data sets. Surprisingly, for first-pass decoding, the rather simple
approach of shallow fusion performs best across data sets. However, for Google
data sets we find that cold fusion has a lower oracle error rate and
outperforms other approaches after second-pass rescoring on the Google voice
search data set.
A Survey of the Usages of Deep Learning in Natural Language Processing
Authors: Daniel W. Otter, Julian R. Medina, Jugal K. Kalita
Date: 2018-07-27 00:00:00
Tasks: Deep Learning

Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.
Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks
Authors: Snehashis Roy, John A. Butman, Dzung L. Pham
Date: 2018-07-27 00:00:00
Tasks: Image Reconstruction

With the increasing popularity of PET-MR scanners in clinical applications,
synthesis of CT images from MR has been an important research topic. Accurate
PET image reconstruction requires attenuation correction, which is based on the
electron density of tissues and can be obtained from CT images. While CT
measures electron density information for x-ray photons, MR images convey
information about the magnetic properties of tissues. Therefore, with the
advent of PET-MR systems, the attenuation coefficients need to be indirectly
estimated from MR images. In this paper, we propose a fully convolutional
neural network (CNN) based method to synthesize head CT from ultra-short
echo-time (UTE) dual-echo MR images. Unlike traditional $T_1$-w images which do
not have any bone signal, UTE images show some signal for bone, which makes it
a good candidate for MR to CT synthesis. A notable advantage of our approach is
that accurate results were achieved with a small training data set. Using an
atlas of a single CT and dual-echo UTE pair, we train a deep neural network
model to learn the transform of MR intensities to CT using patches. We compared
our CNN based model with a state-of-the-art registration based as well as a
Bayesian model based CT synthesis method, and showed that the proposed CNN
model outperforms both of them. We also compared the proposed model when only
$T_1$-w images are available instead of UTE, and show that UTE images produce
better synthesis than using just $T_1$-w images.
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
Authors: Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Ghalib Bello, Georgia Doumou, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert
Date: 2018-07-27 00:00:00
Tasks: Segmentation

In this paper we introduce a novel and accurate optimisation method for
segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension
(PH). The proposed method explicitly takes into account the image features
learned from a deep neural network. To this end, we estimate simultaneous
probability maps over region and edge locations in CMR images using a fully
convolutional network. Due to the distinct morphology of the heart in patients
with PH, these probability maps can then be incorporated in a single nested
level set optimisation framework to achieve multi-region segmentation with high
efficiency. The proposed method uses an automatic way for level set
initialisation and thus the whole optimisation is fully automated. We
demonstrate that the proposed deep nested level set (DNLS) method outperforms
existing state-of-the-art methods for CMR segmentation in PH patients.
Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace
Authors: Bryan Head, Uri Wilensky
Date: 2018-07-27 00:00:00

We present a method of endowing agents in an agent-based model (ABM) with
sophisticated cognitive capabilities and a naturally tunable level of
intelligence. Often, ABMs use random behavior or greedy algorithms for
maximizing objectives (such as a predator always chasing after the closest
prey). However, random behavior is too simplistic in many circumstances and
greedy algorithms, as well as classic AI planning techniques, can be brittle in
the context of the unpredictable and emergent situations in which agents may
find themselves. Our method, called agent-centric Monte Carlo cognition
(ACMCC), centers around using a separate agent-based model to represent the
agents' cognition. This model is then used by the agents in the primary model
to predict the outcomes of their actions, and thus guide their behavior. To
that end, we have implemented our method in the NetLogo agent-based modeling
platform, using the recently released LevelSpace extension, which we developed
to allow NetLogo models to interact with other NetLogo models. As an
illustrative example, we extend the Wolf Sheep Predation model (included with
NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on
agent performance and model dynamics. We find that ACMCC provides a reliable
and understandable method of controlling agent intelligence, and has a large
impact on agent performance and model dynamics even at low settings.
TBI Contusion Segmentation from MRI using Convolutional Neural Networks
Authors: Snehashis Roy, John A. Butman, Leighton Chan, Dzung L. Pham
Date: 2018-07-27 00:00:00
Tasks: Lesion Segmentation

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that
may result in hematomas and contusions and can lead to stroke or chronic
disability. An accurate quantification of the lesion volumes and their
locations is essential to understand the pathophysiology of TBI and its
progression. In this paper, we propose a fully convolutional neural network
(CNN) model to segment contusions and lesions from brain magnetic resonance
(MR) images of patients with TBI. The CNN architecture proposed here was based
on a state of the art CNN architecture from Google, called Inception. Using a
3-layer Inception network, lesions are segmented from multi-contrast MR images.
When compared with two recent TBI lesion segmentation methods, one based on CNN
(called DeepMedic) and another based on random forests, the proposed algorithm
showed improved segmentation accuracy on images of 18 patients with mild to
severe TBI. Using a leave-one-out cross validation, the proposed model achieved
a median Dice of 0.75, which was significantly better (p<0.01) than the two
competing methods.
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
Authors: Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
Date: 2018-07-27 00:00:00
Tasks: Lung Nodule Detection, Segmentation

We introduce CASED, a novel curriculum sampling algorithm that facilitates
the optimization of deep learning segmentation or detection models on data sets
with extreme class imbalance. We evaluate the CASED learning framework on the
task of lung nodule detection in chest CT. In contrast to two-stage solutions,
wherein nodule candidates are first proposed by a segmentation model and
refined by a second detection stage, CASED improves the training of deep nodule
segmentation models (e.g. UNet) to the point where state of the art results are
achieved using only a trivial detection stage. CASED improves the optimization
of deep segmentation models by allowing them to first learn how to distinguish
nodules from their immediate surroundings, while continuously adding a greater
proportion of difficult-to-classify global context, until uniformly sampling
from the empirical data distribution. Using CASED during training yields a
minimalist proposal to the lung nodule detection problem that tops the LUNA16
nodule detection benchmark with an average sensitivity score of 88.35%.
Furthermore, we find that models trained using CASED are robust to nodule
annotation quality by showing that comparable results can be achieved when only
a point and radius for each ground truth nodule are provided during training.
Finally, the CASED learning framework makes no assumptions with regard to
imaging modality or segmentation target and should generalize to other medical
imaging problems where class imbalance is a persistent problem.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Authors: Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang
Date: 2018-07-27 00:00:00
Tasks: Computational Efficiency, Deblurring, Image Deblurring, Image Super-Resolution, Super-Resolution

Single-image super-resolution is a fundamental task for vision applications
to enhance the image quality with respect to spatial resolution. If the input
image contains degraded pixels, the artifacts caused by the degradation could
be amplified by super-resolution methods. Image blur is a common degradation
source. Images captured by moving or still cameras are inevitably affected by
motion blur due to relative movements between sensors and objects. In this
work, we focus on the super-resolution task with the presence of motion blur.
We propose a deep gated fusion convolution neural network to generate a clear
high-resolution frame from a single natural image with severe blur. By
decomposing the feature extraction step into two task-independent streams, the
dual-branch design can facilitate the training process by avoiding learning the
mixed degradation all-in-one and thus enhance the final high-resolution
prediction results. Extensive experiments demonstrate that our method generates
sharper super-resolved images from low-resolution inputs with high
computational efficiency.
Improving Neural Sequence Labelling using Additional Linguistic Information
Authors: Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
Date: 2018-07-27 00:00:00
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Sentence

Sequence labelling is the task of assigning categorical labels to a data
sequence. In Natural Language Processing, sequence labelling can be applied to
various fundamental problems, such as Part of Speech (POS) tagging, Named
Entity Recognition (NER), and Chunking. In this study, we propose a method to
add various linguistic features to the neural sequence framework to improve
sequence labelling. Besides word level knowledge, sense embeddings are added to
provide semantic information. Additionally, selective readings of character
embeddings are added to capture contextual as well as morphological features
for each word in a sentence. Compared to previous methods, these added
linguistic features allow us to design a more concise model and perform more
efficient training. Our proposed architecture achieves state of the art results
on the benchmark datasets of POS, NER, and chunking. Moreover, the convergence
rate of our model is significantly better than the previous state of the art
models.
Clustering Prominent People and Organizations in Topic-Specific Text Corpora
Authors: Abdulkareem Alsudais, Hovig Tchalian
Date: 2018-07-27 00:00:00
Tasks: Articles, Clustering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Named entities in text documents are the names of people, organization, location or other types of objects in the documents that exist in the real world. A persisting research challenge is to use computational techniques to identify such entities in text documents. Once identified, several text mining tools and algorithms can be utilized to leverage these discovered named entities and improve NLP applications. In this paper, a method that clusters prominent names of people and organizations based on their semantic similarity in a text corpus is proposed. The method relies on common named entity recognition techniques and on recent word embeddings models. The semantic similarity scores generated using the word embeddings models for the named entities are used to cluster similar entities of the people and organizations types. Two human judges evaluated ten variations of the method after it was run on a corpus that consists of 4,821 articles on a specific topic. The performance of the method was measured using three quantitative measures. The results of these three metrics demonstrate that the method is effective in clustering semantically similar named entities.
One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning
Authors: Ruijin Cang, Hope Yao, Yi Ren
Date: 2018-07-27 00:00:00
Tasks: BIG-bench Machine Learning

We introduce a theory-driven mechanism for learning a neural network model
that performs generative topology design in one shot given a problem setting,
circumventing the conventional iterative process that computational design
tasks usually entail. The proposed mechanism can lead to machines that quickly
response to new design requirements based on its knowledge accumulated through
past experiences of design generation. Achieving such a mechanism through
supervised learning would require an impractically large amount of
problem-solution pairs for training, due to the known limitation of deep neural
networks in knowledge generalization. To this end, we introduce an interaction
between a student (the neural network) and a teacher (the optimality conditions
underlying topology optimization): The student learns from existing data and is
tested on unseen problems. Deviation of the student's solutions from the
optimality conditions is quantified, and used for choosing new data points to
learn from. We call this learning mechanism "theory-driven", as it explicitly
uses domain-specific theories to guide the learning, thus distinguishing itself
from purely data-driven supervised learning. We show through a compliance
minimization problem that the proposed learning mechanism leads to topology
generation with near-optimal structural compliance, much improved from standard
supervised learning under the same computational budget.