pwc-archive/papers-with-abstracts huggingface dataset:
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition
Authors: Xikun Zhang, Chang Xu, Xinmei Tian, DaCheng Tao
Date: 2018-05-16 00:00:00
Tasks: Action Recognition, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

This paper investigates body bones from skeleton data for skeleton based
action recognition. Body joints, as the direct result of mature pose estimation
technologies, are always the key concerns of traditional action recognition
methods. However, instead of joints, we humans naturally identify how the human
body moves according to shapes, lengths and places of bones, which are more
obvious and stable for observation. Hence given graphs generated from skeleton
data, we propose to develop convolutions over graph edges that correspond to
bones in human skeleton. We describe an edge by integrating its spatial
neighboring edges to explore the cooperation between different bones, as well
as its temporal neighboring edges to address the consistency of movements in an
action. A graph edge convolutional neural network is then designed for skeleton
based action recognition. Considering the complementarity between graph node
convolution and graph edge convolution, we additionally construct two hybrid
neural networks to combine graph node convolutional neural network and graph
edge convolutional neural network using shared intermediate layers.
Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our
graph edge convolution is effective to capture characteristic of actions and
our graph edge convolutional neural network significantly outperforms existing
state-of-art skeleton based action recognition methods. Additionally, more
performance improvements can be achieved by the hybrid networks.
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
Authors: Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J. Durr
Date: 2018-05-22 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Deep learning has emerged as a powerful artificial intelligence tool to
interpret medical images for a growing variety of applications. However, the
paucity of medical imaging data with high-quality annotations that is necessary
for training such methods ultimately limits their performance. Medical data is
challenging to acquire due to privacy issues, shortage of experts available for
annotation, limited representation of rare conditions and cost. This problem
has previously been addressed by using synthetically generated data. However,
networks trained on synthetic data often fail to generalize to real data.
Cinematic rendering simulates the propagation and interaction of light passing
through tissue models reconstructed from CT data, enabling the generation of
photorealistic images. In this paper, we present one of the first applications
of cinematic rendering in deep learning, in which we propose to fine-tune
synthetic data-driven networks using cinematically rendered CT data for the
task of monocular depth estimation in endoscopy. Our experiments demonstrate
that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and
fine-tuned on photorealistic cinematically rendered data adapt better to real
medical images and demonstrate more robust performance when compared to
networks with no fine-tuning, (b) these fine-tuned networks require less
training data to converge to an optimal solution, and (c) fine-tuning with data
from a variety of photorealistic rendering conditions of the same scene
prevents the network from learning patient-specific information and aids in
generalizability of the model. Our empirical evaluation demonstrates that
networks fine-tuned with cinematically rendered data predict depth with 56.87%
less error for rendered endoscopy images and 27.49% less error for real porcine
colon endoscopy images.
On the Impact of Various Types of Noise on Neural Machine Translation
Authors: Huda Khayrallah, Philipp Koehn
Date: 2018-05-31 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Sentence, Translation

We examine how various types of noise in the parallel training data impact
the quality of neural machine translation systems. We create five types of
artificial noise and analyze how they degrade performance in neural and
statistical machine translation. We find that neural models are generally more
harmed by noise than statistical models. For one especially egregious type of
noise they learn to just copy the input sentence.
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
Authors: Tolga Birdal, Umut Şimşekli, M. Onur Eken, Slobodan Ilic
Date: 2018-05-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Simultaneous Localization and Mapping

We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm
for initializing pose graph optimization problems, arising in various scenarios
such as SFM (structure from motion) or SLAM (simultaneous localization and
mapping). TG-MCMC is first of its kind as it unites asymptotically global
non-convex optimization on the spherical manifold of quaternions with posterior
sampling, in order to provide both reliable initial poses and uncertainty
estimates that are informative about the quality of individual solutions. We
devise rigorous theoretical convergence guarantees for our method and
extensively evaluate it on synthetic and real benchmark datasets. Besides its
elegance in formulation and theory, we show that our method is robust to
missing data, noise and the estimated uncertainties capture intuitive
properties of the data.
Learning Factorized Representations for Open-set Domain Adaptation
Authors: Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann
Date: 2018-05-31 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation

Domain adaptation for visual recognition has undergone great progress in the
past few years. Nevertheless, most existing methods work in the so-called
closed-set scenario, assuming that the classes depicted by the target images
are exactly the same as those of the source domain. In this paper, we tackle
the more challenging, yet more realistic case of open-set domain adaptation,
where new, unknown classes can be present in the target data. While, in the
unsupervised scenario, one cannot expect to be able to identify each specific
new class, we aim to automatically detect which samples belong to these new
classes and discard them from the recognition process. To this end, we rely on
the intuition that the source and target samples depicting the known classes
can be generated by a shared subspace, whereas the target samples from unknown
classes come from a different, private subspace. We therefore introduce a
framework that factorizes the data into shared and private parts, while
encouraging the shared representation to be discriminative. Our experiments on
standard benchmarks evidence that our approach significantly outperforms the
state-of-the-art in open-set domain adaptation.
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding
Authors: Partha Pratim Roy, Ayan Kumar Bhunia, Avirup Bhattacharyya, Umapada Pal
Date: 2017-08-18 00:00:00
Tasks: Keyword Spotting, Optical Character Recognition (OCR), Retrieval, Text Retrieval

Retrieval of text information from natural scene images and video frames is a
challenging task due to its inherent problems like complex character shapes,
low resolution, background noise, etc. Available OCR systems often fail to
retrieve such information in scene/video frames. Keyword spotting, an
alternative way to retrieve information, performs efficient text searching in
such scenarios. However, current word spotting techniques in scene/video images
are script-specific and they are mainly developed for Latin script. This paper
presents a novel word spotting framework using dynamic shape coding for text
retrieval in natural scene image and video frames. The framework is designed to
search query keyword from multiple scripts with the help of on-the-fly
script-wise keyword generation for the corresponding script. We have used a
two-stage word spotting approach using Hidden Markov Model (HMM) to detect the
translated keyword in a given text line by identifying the script of the line.
A novel unsupervised dynamic shape coding based scheme has been used to group
similar shape characters to avoid confusion and to improve text alignment.
Next, the hypotheses locations are verified to improve retrieval performance.
To evaluate the proposed system for searching keyword from natural scene image
and video frames, we have considered two popular Indic scripts such as Bangla
(Bengali) and Devanagari along with English. Inspired by the zone-wise
recognition approach in Indic scripts[1], zone-wise text information has been
used to improve the traditional word spotting performance in Indic scripts. For
our experiment, a dataset consisting of images of different scenes and video
frames of English, Bangla and Devanagari scripts were considered. The results
obtained showed the effectiveness of our proposed word spotting approach.
Optimized Participation of Multiple Fusion Functions in Consensus Creation: An Evolutionary Approach
Authors: Elaheh Rashedi, Abdolreza Mirzaei
Date: 2018-05-31 00:00:00
Tasks: Clustering, Clustering Ensemble

Recent studies show that ensemble methods enhance the stability and
robustness of unsupervised learning. These approaches are successfully utilized
to construct multiple clustering and combine them into a one representative
consensus clustering of an improved quality. The quality of the consensus
clustering is directly depended on fusion functions used in combination. In
this article, the hierarchical clustering ensemble techniques are extended by
introducing a new evolutionary fusion function. In the proposed method,
multiple hierarchical clustering methods are generated via bagging. Thereafter,
the consensus clustering is obtained using the search capability of genetic
algorithm among different aggregated clustering methods made by different
fusion functions. Putting some popular data sets to empirical study, the
quality of the proposed method is compared with regular clustering ensembles.
Experimental results demonstrate the accuracy improvement of the aggregated
clustering results.
Grouped Convolutional Neural Networks for Multivariate Time Series
Authors: Subin Yi, Janghoon Ju, Man-Ki Yoon, Jaesik Choi
Date: 2017-03-29 00:00:00
Tasks: Anomaly Detection, Clustering, Fault Diagnosis, Time Series, Time Series Analysis

Analyzing multivariate time series data is important for many applications
such as automated control, fault diagnosis and anomaly detection. One of the
key challenges is to learn latent features automatically from dynamically
changing multivariate input. In visual recognition tasks, convolutional neural
networks (CNNs) have been successful to learn generalized feature extractors
with shared parameters over the spatial domain. However, when high-dimensional
multivariate time series is given, designing an appropriate CNN model structure
becomes challenging because the kernels may need to be extended through the
full dimension of the input volume. To address this issue, we present two
structure learning algorithms for deep CNN models. Our algorithms exploit the
covariance structure over multiple time series to partition input volume into
groups. The first algorithm learns the group CNN structures explicitly by
clustering individual input sequences. The second algorithm learns the group
CNN structures implicitly from the error backpropagation. In experiments with
two real-world datasets, we demonstrate that our group CNNs outperform existing
CNN based regression methods.
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
Authors: Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Syeda Furruka Banu, Adel Saleh, Vivek Kumar Singh, Forhad U H Chowdhury, Saddam Abdulwahab, Santiago Romani, Petia Radeva, Domenec Puig
Date: 2018-05-25 00:00:00
Tasks: Decoder, GPU, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for
automated diagnosis of melanoma. In this paper, we present a robust deep
learning SLS model, so-called SLSDeep, which is represented as an
encoder-decoder network. The encoder network is constructed by dilated residual
layers, in turn, a pyramid pooling network followed by three convolution layers
is used for the decoder. Unlike the traditional methods employing a
cross-entropy loss, we investigated a loss function by combining both Negative
Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the
melanoma regions with sharp boundaries. The robustness of the proposed model
was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion
analysis towards melanoma detection challenge. The proposed model outperforms
the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is
capable to segment more than $100$ images of size 384x384 per second on a
recent GPU.
Geometric Understanding of Deep Learning
Authors: Na Lei, Zhongxuan Luo, Shing-Tung Yau, David Xianfeng Gu
Date: 2018-05-26 00:00:00
Tasks: Deep Learning, Machine Translation, speech-recognition, Speech Recognition, Translation

Deep learning is the mainstream technique for many machine learning tasks,
including image recognition, machine translation, speech recognition, and so
on. It has outperformed conventional methods in various fields and achieved
great successes. Unfortunately, the understanding on how it works remains
unclear. It has the central importance to lay down the theoretic foundation for
deep learning.
  In this work, we give a geometric view to understand deep learning: we show
that the fundamental principle attributing to the success is the manifold
structure in data, namely natural high dimensional data concentrates close to a
low-dimensional manifold, deep learning learns the manifold and the probability
distribution on it.
  We further introduce the concepts of rectified linear complexity for deep
neural network measuring its learning capability, rectified linear complexity
of an embedding manifold describing the difficulty to be learned. Then we show
for any deep neural network with fixed architecture, there exists a manifold
that cannot be learned by the network. Finally, we propose to apply optimal
mass transportation theory to control the probability distribution in the
latent space.
End-to-End Learning for the Deep Multivariate Probit Model
Authors: Di Chen, Yexiang Xue, Carla P. Gomes
Date: 2018-03-22 00:00:00
Proceeding: ICML 2018 7
Tasks: GPU

The multivariate probit model (MVP) is a popular classic model for studying
binary responses of multiple entities. Nevertheless, the computational
challenge of learning the MVP model, given that its likelihood involves
integrating over a multidimensional constrained space of latent variables,
significantly limits its application in practice. We propose a flexible deep
generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP),
which is an end-to-end learning scheme that uses an efficient parallel sampling
process of the multivariate probit model to exploit GPU-boosted deep neural
networks. We present both theoretical and empirical analysis of the convergence
behavior of DMVP's sampling process with respect to the resolution of the
correlation structure. We provide convergence guarantees for DMVP and our
empirical analysis demonstrates the advantages of DMVP's sampling compared with
standard MCMC-based methods. We also show that when applied to multi-entity
modelling problems, which are natural DMVP applications, DMVP trains faster
than classical MVP, by at least an order of magnitude, captures rich
correlations among entities, and further improves the joint likelihood of
entities compared with several competitive models.
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Authors: Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn
Date: 2018-03-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Meta-Learning, Meta Reinforcement Learning, Model-based Reinforcement Learning, Pose Estimation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Although reinforcement learning methods can achieve impressive results in
simulation, the real world presents two major challenges: generating samples is
exceedingly expensive, and unexpected perturbations or unseen situations cause
proficient but specialized policies to fail at test time. Given that it is
impractical to train separate policies to accommodate all situations the agent
may see in the real world, this work proposes to learn how to quickly and
effectively adapt online to new tasks. To enable sample-efficient learning, we
consider learning online adaptation in the context of model-based reinforcement
learning. Our approach uses meta-learning to train a dynamics model prior such
that, when combined with recent data, this prior can be rapidly adapted to the
local context. Our experiments demonstrate online adaptation for continuous
control tasks on both simulated and real-world agents. We first show simulated
agents adapting their behavior online to novel terrains, crippled body parts,
and highly-dynamic environments. We also illustrate the importance of
incorporating online adaptation into autonomous agents that operate in the real
world by applying our method to a real dynamic legged millirobot. We
demonstrate the agent's learned ability to quickly adapt online to a missing
leg, adjust to novel terrains and slopes, account for miscalibration or errors
in pose estimation, and compensate for pulling payloads.
Rehabilitating the ColorChecker Dataset for Illuminant Estimation
Authors: Ghalia Hemrit, Graham D. Finlayson, Arjan Gijsenij, Peter Gehler, Simone Bianco, Brian Funt, Mark Drew, Lilong Shi
Date: 2018-05-30 00:00:00

In a previous work, it was shown that there is a curious problem with the
benchmark ColorChecker dataset for illuminant estimation. To wit, this dataset
has at least 3 different sets of ground-truths. Typically, for a single
algorithm a single ground-truth is used. But then different algorithms, whose
performance is measured with respect to different ground-truths, are compared
against each other and then ranked. This makes no sense. We show in this paper
that there are also errors in how each ground-truth set was calculated. As a
result, all performance rankings based on the ColorChecker dataset - and there
are scores of these - are inaccurate.
  In this paper, we re-generate a new 'recommended' set of ground-truth based
on the calculation methodology described by Shi and Funt. We then review the
performance evaluation of a range of illuminant estimation algorithms. Compared
with the legacy ground-truths, we find that the difference in how algorithms
perform can be large, with many local rankings of algorithms being reversed.
  Finally, we draw the readers attention to our new 'open' data repository
which, we hope, will allow the ColorChecker set to be rehabilitated and once
again to become a useful benchmark for illuminant estimation algorithms.
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Authors: Jonathan Krause, Varun Gulshan, Ehsan Rahimy, Peter Karth, Kasumi Widner, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2017-10-04 00:00:00
Tasks: BIG-bench Machine Learning

Diabetic retinopathy (DR) and diabetic macular edema are common complications
of diabetes which can lead to vision loss. The grading of DR is a fairly
complex process that requires the detection of fine features such as
microaneurysms, intraretinal hemorrhages, and intraretinal microvascular
abnormalities. Because of this, there can be a fair amount of grader
variability. There are different methods of obtaining the reference standard
and resolving disagreements between graders, and while it is usually accepted
that adjudication until full consensus will yield the best reference standard,
the difference between various methods of resolving disagreements has not been
examined extensively. In this study, we examine the variability in different
methods of grading, definitions of reference standards, and their effects on
building deep learning models for the detection of diabetic eye disease. We
find that a small set of adjudicated DR grades allows substantial improvements
in algorithm performance. The resulting algorithm's performance was on par with
that of individual U.S. board-certified ophthalmologists and retinal
specialists.
Multi-Mention Learning for Reading Comprehension with Neural Cascades
Authors: Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski
Date: 2017-11-02 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reading Comprehension, TriviaQA

Reading comprehension is a challenging task, especially when executed across
longer or across multiple evidence documents, where the answer is likely to
reoccur. Existing neural architectures typically do not scale to the entire
evidence, and hence, resort to selecting a single passage in the document
(either via truncation or other means), and carefully searching for the answer
within that passage. However, in some cases, this strategy can be suboptimal,
since by focusing on a specific passage, it becomes difficult to leverage
multiple mentions of the same answer throughout the document. In this work, we
take a different approach by constructing lightweight models that are combined
in a cascade to find the answer. Each submodel consists only of feed-forward
networks equipped with an attention mechanism, making it trivially
parallelizable. We show that our approach can scale to approximately an order
of magnitude larger evidence documents and can aggregate information at the
representation level from multiple mentions of each answer candidate across the
document. Empirically, our approach achieves state-of-the-art performance on
both the Wikipedia and web domains of the TriviaQA dataset, outperforming more
complex, recurrent architectures.
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Authors: Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Christopher Pal
Date: 2018-03-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Depth Estimation, Translation

We present an unsupervised approach for learning to estimate three
dimensional (3D) facial structure from a single image while also predicting 3D
viewpoint transformations that match a desired pose and facial geometry. We
achieve this by inferring the depth of facial keypoints of an input image in an
unsupervised manner, without using any form of ground-truth depth information.
We show how it is possible to use these depths as intermediate computations
within a new backpropable loss to predict the parameters of a 3D affine
transformation matrix that maps inferred 3D keypoints of an input face to the
corresponding 2D keypoints on a desired target facial geometry or pose. Our
resulting approach, called DepthNets, can therefore be used to infer plausible
3D transformations from one face pose to another, allowing faces to be
frontalized, transformed into 3D models or even warped to another pose and
facial geometry. Lastly, we identify certain shortcomings with our formulation,
and explore adversarial image translation techniques as a post-processing step
to re-synthesize complete head shots for faces re-targeted to different poses
or identities.