pwc-archive/papers-with-abstracts huggingface dataset:
Emotional End-to-End Neural Speech Synthesizer
Authors: Young-Gun Lee, Azam Rabiee, Soo-Young Lee
Date: 2017-11-15 00:00:00

In this paper, we introduce an emotional speech synthesizer based on the
recent end-to-end neural model, named Tacotron. Despite its benefits, we found
that the original Tacotron suffers from the exposure bias problem and
irregularity of the attention alignment. Later, we address the problem by
utilization of context vector and residual connection at recurrent neural
networks (RNNs). Our experiments showed that the model could successfully train
and generate speech for given emotion labels.
Analytical Moment Regularizer for Gaussian Robust Networks
Authors: Modar Alfadly, Adel Bibi, Bernard Ghanem
Date: 2019-04-24 00:00:00
Tasks: Data Augmentation

Despite the impressive performance of deep neural networks (DNNs) on numerous
vision tasks, they still exhibit yet-to-understand uncouth behaviours. One
puzzling behaviour is the subtle sensitive reaction of DNNs to various noise
attacks. Such a nuisance has strengthened the line of research around
developing and training noise-robust networks. In this work, we propose a new
training regularizer that aims to minimize the probabilistic expected training
loss of a DNN subject to a generic Gaussian input. We provide an efficient and
simple approach to approximate such a regularizer for arbitrary deep networks.
This is done by leveraging the analytic expression of the output mean of a
shallow neural network; avoiding the need for the memory and computationally
expensive data augmentation. We conduct extensive experiments on LeNet and
AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100
demonstrating the effectiveness of our proposed regularizer. In particular, we
show that networks that are trained with the proposed regularizer benefit from
a boost in robustness equivalent to performing 3-21 folds of data augmentation.
Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields
Authors: Evan Shelhamer, Dequan Wang, Trevor Darrell
Date: 2019-04-25 00:00:00
Tasks: Form, Semantic Segmentation

The visual world is vast and varied, but its variations divide into
structured and unstructured factors. We compose free-form filters and
structured Gaussian filters, optimized end-to-end, to factorize deep
representations and learn both local features and their degree of locality. Our
semi-structured composition is strictly more expressive than free-form
filtering, and changes in its structured parameters would require changes in
free-form architecture. In effect this optimizes over receptive field size and
shape, tuning locality to the data and task. Dynamic inference, in which the
Gaussian structure varies with the input, adapts receptive field size to
compensate for local scale variation. Optimizing receptive field size improves
semantic segmentation accuracy on Cityscapes by 1-2 points for strong dilated
and skip architectures and by up to 10 points for suboptimal designs. Adapting
receptive fields by dynamic Gaussian structure further improves results,
equaling the accuracy of free-form deformation while improving efficiency.
Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Authors: Bahareh Behboodi, Hassan Rivaz
Date: 2019-04-24 00:00:00
Tasks: Segmentation

Segmentation of ultrasound images is an essential task in both diagnosis and
image-guided interventions given the ease-of-use and low cost of this imaging
modality. As manual segmentation is tedious and time consuming, a growing body
of research has focused on the development of automatic segmentation
algorithms. Deep learning algorithms have shown remarkable achievements in this
regard; however, they need large training datasets. Unfortunately, preparing
large labeled datasets in ultrasound images is prohibitively difficult.
Therefore, in this study, we propose the use of simulated ultrasound (US)
images for training the U-Net deep learning segmentation architecture and test
on tissue-mimicking phantom data collected by an ultrasound machine. We
demonstrate that the trained architecture on the simulated data is
transferrable to real data, and therefore, simulated data can be considered as
an alternative training dataset when real datasets are not available. The
second contribution of this paper is that we train our U- Net network on
envelope and B-mode images of the simulated dataset, and test the trained
network on real envelope and B- mode images of phantom, respectively. We show
that test results are superior for the envelope data compared to B-mode image.
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
Authors: Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari
Date: 2019-04-25 00:00:00
Tasks: Cancer Classification, Classification, General Classification, Image Segmentation, Segmentation, Semantic Segmentation, Skin Cancer Classification, Skin Cancer Segmentation

In the last few years, Deep Learning (DL) has been showing superior
performance in different modalities of biomedical image analysis. Several DL
architectures have been proposed for classification, segmentation, and
detection tasks in medical imaging and computational pathology. In this paper,
we propose a new DL architecture, the NABLA-N network, with better feature
fusion techniques in decoding units for dermoscopic image segmentation tasks.
The NABLA-N network has several advances for segmentation tasks. First, this
model ensures better feature representation for semantic segmentation with a
combination of low to high-level feature maps. Second, this network shows
better quantitative and qualitative results with the same or fewer network
parameters compared to other methods. In addition, the Inception Recurrent
Residual Convolutional Neural Network (IRRCNN) model is used for skin cancer
classification. The proposed NABLA-N network and IRRCNN models are evaluated
for skin cancer segmentation and classification on the benchmark datasets from
the International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental
results show superior performance on segmentation tasks compared to the
Recurrent Residual U-Net (R2U-Net). The classification model shows around 87%
testing accuracy for dermoscopic skin cancer classification on ISIC2018
dataset.
Making Convolutional Networks Shift-Invariant Again
Authors: Richard Zhang
Date: 2019-04-25 00:00:00
Tasks: Classification Consistency, Conditional Image Generation, Domain Generalization, Image Classification, Image Generation

Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at https://richzhang.github.io/antialiased-cnns/ .
Deep Sparse Representation-based Classification
Authors: Mahdi Abavisani, Vishal M. Patel
Date: 2019-04-24 00:00:00
Tasks: Classification, Decoder, General Classification, Image Classification, Semi-Supervised Image Classification, Sparse Representation-based Classification

We present a transductive deep learning-based formulation for the sparse
representation-based classification (SRC) method. The proposed network consists
of a convolutional autoencoder along with a fully-connected layer. The role of
the autoencoder network is to learn robust deep features for classification. On
the other hand, the fully-connected layer, which is placed in between the
encoder and the decoder networks, is responsible for finding the sparse
representation. The estimated sparse codes are then used for classification.
Various experiments on three different datasets show that the proposed network
leads to sparse representations that give better classification results than
state-of-the-art SRC methods. The source code is available at:
github.com/mahdiabavisani/DSRC.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Authors: Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu
Date: 2019-04-25 00:00:00
Tasks: Instance Segmentation, Object Detection, Object Recognition

The Non-Local Network (NLNet) presents a pioneering approach for capturing
long-range dependencies, via aggregating query-specific global context to each
query position. However, through a rigorous empirical analysis, we have found
that the global contexts modeled by non-local network are almost the same for
different query positions within an image. In this paper, we take advantage of
this finding to create a simplified network based on a query-independent
formulation, which maintains the accuracy of NLNet but with significantly less
computation. We further observe that this simplified design shares similar
structure with Squeeze-Excitation Network (SENet). Hence we unify them into a
three-step general framework for global context modeling. Within the general
framework, we design a better instantiation, called the global context (GC)
block, which is lightweight and can effectively model the global context. The
lightweight property allows us to apply it for multiple layers in a backbone
network to construct a global context network (GCNet), which generally
outperforms both simplified NLNet and SENet on major benchmarks for various
recognition tasks. The code and configurations are released at
https://github.com/xvjiarui/GCNet.
Local Relation Networks for Image Recognition
Authors: Han Hu, Zheng Zhang, Zhenda Xie, Stephen Lin
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, Image Classification, Relation, Relation Network

The convolution layer has been the dominant feature extractor in computer
vision for years. However, the spatial aggregation in convolution is basically
a pattern matching process that applies fixed filters which are inefficient at
modeling visual elements with varying spatial distributions. This paper
presents a new image feature extractor, called the local relation layer, that
adaptively determines aggregation weights based on the compositional
relationship of local pixel pairs. With this relational approach, it can
composite visual elements into higher-level entities in a more efficient manner
that benefits semantic inference. A network built with local relation layers,
called the Local Relation Network (LR-Net), is found to provide greater
modeling capacity than its counterpart built with regular convolution on
large-scale recognition tasks such as ImageNet classification.
RepPoints: Point Set Representation for Object Detection
Authors: Ze Yang, Shaohui Liu, Han Hu, Li-Wei Wang, Stephen Lin
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection

Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present \textbf{RepPoints} (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 $AP_{50}$ on the COCO test-dev detection benchmark, using ResNet-101 model. Code is available at https://github.com/microsoft/RepPoints.
Spatial-Temporal Relation Networks for Multi-Object Tracking
Authors: Jiarui Xu, Yue Cao, Zheng Zhang, Han Hu
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Relation

Recent progress in multiple object tracking (MOT) has shown that a robust
similarity score is key to the success of trackers. A good similarity score is
expected to reflect multiple cues, e.g. appearance, location, and topology,
over a long period of time. However, these cues are heterogeneous, making them
hard to be combined in a unified network. As a result, existing methods usually
encode them in separate networks or require a complex training approach. In
this paper, we present a unified framework for similarity measurement which
could simultaneously encode various cues and perform reasoning across both
spatial and temporal domains. We also study the feature representation of a
tracklet-object pair in depth, showing a proper design of the pair features can
well empower the trackers. The resulting approach is named spatial-temporal
relation networks (STRN). It runs in a feed-forward way and can be trained in
an end-to-end manner. The state-of-the-art accuracy was achieved on all of the
MOT15-17 benchmarks using public detection and online settings.
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Authors: Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
Date: 2019-04-25 00:00:00
Tasks: Decision Making, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Navigating urban environments represents a complex task for automated
vehicles. They must reach their goal safely and efficiently while considering a
multitude of traffic participants. We propose a modular decision making
algorithm to autonomously navigate intersections, addressing challenges of
existing rule-based and reinforcement learning (RL) approaches. We first
present a safe RL algorithm relying on a model-checker to ensure safety
guarantees. To make the decision strategy robust to perception errors and
occlusions, we introduce a belief update technique using a learning based
approach. Finally, we use a scene decomposition approach to scale our algorithm
to environments with multiple traffic participants. We empirically demonstrate
that our algorithm outperforms rule-based methods and reinforcement learning
techniques on a complex intersection scenario.
Radar-only ego-motion estimation in difficult settings via graph matching
Authors: Sarah H. Cen, Paul Newman
Date: 2019-04-25 00:00:00
Tasks: Graph Matching, Motion Estimation

Radar detects stable, long-range objects under variable weather and lighting
conditions, making it a reliable and versatile sensor well suited for
ego-motion estimation. In this work, we propose a radar-only odometry pipeline
that is highly robust to radar artifacts (e.g., speckle noise and false
positives) and requires only one input parameter. We demonstrate its ability to
adapt across diverse settings, from urban UK to off-road Iceland, achieving a
scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS
as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We
present algorithms for keypoint extraction and data association, framing the
latter as a graph matching optimization problem, and provide an in-depth system
analysis.
Importance of Copying Mechanism for News Headline Generation
Authors: Ilya Gusev
Date: 2019-04-25 00:00:00
Tasks: Articles, Headline Generation, Text Summarization

News headline generation is an essential problem of text summarization
because it is constrained, well-defined, and is still hard to solve. Models
with a limited vocabulary can not solve it well, as new named entities can
appear regularly in the news and these entities often should be in the
headline. News articles in morphologically rich languages such as Russian
require model modifications due to a large number of possible word forms. This
study aims to validate that models with a possibility of copying words from the
original article performs better than models without such an option. The
proposed model achieves a mean ROUGE score of 23 on the provided test dataset,
which is 8 points greater than the result of a similar model without a copying
mechanism. Moreover, the resulting model performs better than any known model
on the new dataset of Russian news.
Terminologies augmented recurrent neural network model for clinical named entity recognition
Authors: Ivan Lerner, Nicolas Paris, Xavier Tannier
Date: 2019-04-25 00:00:00
Tasks: Diagnostic, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and an hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (drug name, sign or symptom, disease or disorder, diagnostic procedure or lab test and therapeutic procedure). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities which took 28 hours to annotate, the inter-annotator agreement was acceptable for Drug name in exact match (85%) and acceptable for other entity types in non-exact match (>70%). For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. Moreover, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 92.2% versus 91.1% (i2b2-2009) and 88.4% versus 81.9% (APcNER). On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match, and 84.1% in non-exact match. APcNER is a French corpus for clinical-NER of five type of entities which covers a large variety of document types. Extending supervised model with terminology allowed for an easy performance gain, especially in low regimes of entities, and established near state of the art results on the i2b2-2009 corpus.
The Zero Resource Speech Challenge 2019: TTS without T
Authors: Ewan Dunbar, Robin Algayres, Julien Karadayi, Mathieu Bernard, Juan Benjumea, Xuan-Nga Cao, Lucie Miskic, Charlotte Dugrain, Lucas Ondel, Alan W. black, Laurent Besacier, Sakriani Sakti, Emmanuel Dupoux
Date: 2019-04-25 00:00:00
Tasks: text-to-speech, Text to Speech

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice. We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions. We present an overview of the 19 submitted systems from 10 teams and discuss the main results.
Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation
Authors: Gregory P. Meyer, Jake Charland, Darshan Hegde, Ankit Laddha, Carlos Vallespi-Gonzalez
Date: 2019-04-25 00:00:00
Tasks: 3D Object Detection, 3D Semantic Segmentation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation, Sensor Fusion

In this paper, we present an extension to LaserNet, an efficient and
state-of-the-art LiDAR based 3D object detector. We propose a method for fusing
image data with the LiDAR data and show that this sensor fusion method improves
the detection performance of the model especially at long ranges. The addition
of image data is straightforward and does not require image labels.
Furthermore, we expand the capabilities of the model to perform 3D semantic
segmentation in addition to 3D object detection. On a large benchmark dataset,
we demonstrate our approach achieves state-of-the-art performance on both
object detection and semantic segmentation while maintaining a low runtime.
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Authors: Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu
Date: 2019-04-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Rather than proposing a new method, this paper investigates an issue present
in existing learning algorithms. We study the learning dynamics of
reinforcement learning (RL), specifically a characteristic coupling between
learning and data generation that arises because RL agents control their future
data distribution. In the presence of function approximation, this coupling can
lead to a problematic type of 'ray interference', characterized by learning
dynamics that sequentially traverse a number of performance plateaus,
effectively constraining the agent to learn one thing at a time even when
learning in parallel is better. We establish the conditions under which ray
interference occurs, show its relation to saddle points and obtain the exact
learning dynamics in a restricted setting. We characterize a number of its
properties and discuss possible remedies.
Reward-Based Deception with Cognitive Bias
Authors: Bo Wu, Murat Cubuktepe, Suda Bharadwaj, Ufuk Topcu
Date: 2019-04-25 00:00:00

Deception plays a key role in adversarial or strategic interactions for the
purpose of self-defence and survival. This paper introduces a general framework
and solution to address deception. Most existing approaches for deception
consider obfuscating crucial information to rational adversaries with abundant
memory and computation resources. In this paper, we consider deceiving
adversaries with bounded rationality and in terms of expected rewards. This
problem is commonly encountered in many applications especially involving human
adversaries. Leveraging the cognitive bias of humans in reward evaluation under
stochastic outcomes, we introduce a framework to optimally assign resources of
a limited quantity to optimally defend against human adversaries. Modeling such
cognitive biases follows the so-called prospect theory from behavioral
psychology literature. Then we formulate the resource allocation problem as a
signomial program to minimize the defender's cost in an environment modeled as
a Markov decision process. We use police patrol hour assignment as an
illustrative example and provide detailed simulation results based on
real-world data.