pwc-archive/papers-with-abstracts huggingface dataset:
3D Face From X: Learning Face Shape from Diverse Sources
Authors: Yudong Guo, Lin Cai, Juyong Zhang
Date: 2018-08-16 00:00:00
Tasks: 3D Face Modelling, 3D Face Reconstruction, Face Model, Face Reconstruction

We present a novel method to jointly learn a 3D face parametric model and 3D face reconstruction from diverse sources. Previous methods usually learn 3D face modeling from one kind of source, such as scanned data or in-the-wild images. Although 3D scanned data contain accurate geometric information of face shapes, the capture system is expensive and such datasets usually contain a small number of subjects. On the other hand, in-the-wild face images are easily obtained and there are a large number of facial images. However, facial images do not contain explicit geometric information. In this paper, we propose a method to learn a unified face model from diverse sources. Besides scanned face data and face images, we also utilize a large number of RGB-D images captured with an iPhone X to bridge the gap between the two sources. Experimental results demonstrate that with training data from more sources, we can learn a more powerful face model.
Decision-Making with Belief Functions: a Review
Authors: Thierry Denoeux
Date: 2018-08-16 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Descriptive

Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory. A distinction is made between methods that construct a complete preference relation among acts, and those that allow incomparability of some acts due to lack of information. Methods developed in the imprecise probability framework are applicable in the Dempster-Shafer context and are also reviewed. Shafer's constructive decision theory, which substitutes the notion of goal for that of utility, is described and contrasted with other approaches. The paper ends by pointing out the need to carry out deeper investigation of fundamental issues related to decision-making with belief functions and to assess the descriptive, normative and prescriptive values of the different approaches.
Toward domain-invariant speech recognition via large scale training
Authors: Arun Narayanan, Ananya Misra, Khe Chai Sim, Golan Pundak, Anshuman Tripathi, Mohamed Elfeky, Parisa Haghani, Trevor Strohman, Michiel Bacchiani
Date: 2018-08-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Current state-of-the-art automatic speech recognition systems are trained to
work in specific `domains', defined based on factors like application, sampling
rate and codec. When such recognizers are used in conditions that do not match
the training domain, performance significantly drops. This work explores the
idea of building a single domain-invariant model for varied use-cases by
combining large scale training data from multiple application domains. Our
final system is trained using 162,000 hours of speech. Additionally, each
utterance is artificially distorted during training to simulate effects like
background noise, codec distortion, and sampling rates. Our results show that,
even at such a scale, a model thus trained works almost as well as those
fine-tuned to specific subsets: A single model can be robust to multiple
application domains, and variations like codecs and noise. More importantly,
such models generalize better to unseen conditions and allow for rapid
adaptation -- we show that by using as little as 10 hours of data from a new
domain, an adapted domain-invariant model can match performance of a
domain-specific model trained from scratch using 70 times as much data. We also
highlight some of the limitations of such models and areas that need addressing
in future work.
Incorporating Consistency Verification into Neural Data-to-Document Generation
Authors: Feng Nie, Hailin Chen, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin, Rong pan
Date: 2018-08-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation Extraction

Recent neural models for data-to-document generation have achieved remarkable
progress in producing fluent and informative texts. However, large proportions
of generated texts do not actually conform to the input data. To address this
issue, we propose a new training framework which attempts to verify the
consistency between the generated texts and the input data to guide the
training process. To measure the consistency, a relation extraction model is
applied to check information overlaps between the input data and the generated
texts. The non-differentiable consistency signal is optimized via reinforcement
learning. Experimental results on a recently released challenging dataset
ROTOWIRE show improvements from our framework in various metrics.
Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
Authors: Susan Athey, Guido Imbens
Date: 2018-08-15 00:00:00

In this paper we study estimation of and inference for average treatment
effects in a setting with panel data. We focus on the setting where units,
e.g., individuals, firms, or states, adopt the policy or treatment of interest
at a particular point in time, and then remain exposed to this treatment at all
times afterwards. We take a design perspective where we investigate the
properties of estimators and procedures given assumptions on the assignment
process. We show that under random assignment of the adoption date the standard
Difference-In-Differences estimator is is an unbiased estimator of a particular
weighted average causal effect. We characterize the proeperties of this
estimand, and show that the standard variance estimator is conservative.
DNN Feature Map Compression using Learned Representation over GF(2)
Authors: Denis A. Gudovskiy, Alec Hodgkinson, Luca Rigazio
Date: 2018-08-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Dimensionality Reduction, General Classification, object-detection, Object Detection, Quantization

In this paper, we introduce a method to compress intermediate feature maps of
deep neural networks (DNNs) to decrease memory storage and bandwidth
requirements during inference. Unlike previous works, the proposed method is
based on converting fixed-point activations into vectors over the smallest
GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers
embedded into a DNN. Such an end-to-end learned representation finds more
compact feature maps by exploiting quantization redundancies within the
fixed-point activations along the channel or spatial dimensions. We apply the
proposed network architectures derived from modified SqueezeNet and MobileNetV2
to the tasks of ImageNet classification and PASCAL VOC object detection.
Compared to prior approaches, the conducted experiments show a factor of 2
decrease in memory requirements with minor degradation in accuracy while adding
only bitwise computations.
Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System
Authors: Brian Reinhardt, Isaac Gerg, Daniel Brown, Joonho Park
Date: 2018-08-15 00:00:00
Tasks: Image Compression

Performance of automatic target recognition from synthetic aperture sonar
data is heavily dependent on the complexity of the beamformed imagery. Several
mechanisms can contribute to this, including unwanted vehicle dynamics, the
bathymetry of the scene, and the presence of natural and manmade clutter. To
understand the impact of the environmental complexity on image perception,
researchers have taken approaches rooted in information theory, or heuristics.
Despite these efforts, a quantitative measure for complexity has not been
related to the phenomenology from which it is derived.
  By using subject matter experts (SMEs) we derive a complexity metric for a
set of imagery which accounts for the underlying phenomenology. The goal of
this work is to develop an understanding of how several common information
theoretic and heuristic measures are related to the SME perceived complexity in
synthetic aperture sonar imagery. To achieve this, an ensemble of 10-meter x
10-meter images were cropped from a high-frequency SAS data set that spans
multiple environments. The SME's were presented pairs of images from which they
could rate the relative image complexity. These comparisons were then converted
into the desired sequential ranking using a method first developed by A. Elo
for establishing rankings of chess players.
  The Elo method produced a plausible rank ordering across the broad dataset.
The heuristic and information theoretical metrics were then compared to the
image rank from which they were derived. The metrics with the highest degree of
correlation were those relating to spatial information, e.g. variations in
pixel intensity, with an R-squared value of approximately 0.9. However, this
agreement was dependent on the scale from which the spatial variation was
measured. Results will also be presented for many other measures including
lacunarity, image compression, and entropy.
Frank-Wolfe Style Algorithms for Large Scale Optimization
Authors: Lijun Ding, Madeleine Udell
Date: 2018-08-15 00:00:00

We introduce a few variants on Frank-Wolfe style algorithms suitable for
large scale optimization. We show how to modify the standard Frank-Wolfe
algorithm using stochastic gradients, approximate subproblem solutions, and
sketched decision variables in order to scale to enormous problems while
preserving (up to constants) the optimal convergence rate
$\mathcal{O}(\frac{1}{k})$.
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
Authors: Mikael Agn, Per Munck af Rosenschöld, Oula Puonti, Michael J. Lundemann, Laura Mancini, Anastasia Papadaki, Steffi Thust, John Ashburner, Ian Law, Koen van Leemput
Date: 2018-07-18 00:00:00
Tasks: Brain Segmentation, Segmentation, Tumor Segmentation

In this paper we present a method for simultaneously segmenting brain tumors
and an extensive set of organs-at-risk for radiation therapy planning of
glioblastomas. The method combines a contrast-adaptive generative model for
whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate
experimentally that the method is able to adapt to image acquisitions that
differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is
comparable to that of the current state of the art; and that it captures most
organs-at-risk sufficiently well for radiation therapy planning purposes. The
proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation
therapy.
DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast
Authors: Eduardo R. Rodrigues, Igor Oliveira, Renato L. F. Cunha, Marco A. S. Netto
Date: 2018-08-15 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Running high-resolution physical models is computationally expensive and
essential for many disciplines. Agriculture, transportation, and energy are
sectors that depend on high-resolution weather models, which typically consume
many hours of large High Performance Computing (HPC) systems to deliver timely
results. Many users cannot afford to run the desired resolution and are forced
to use low resolution output. One simple solution is to interpolate results for
visualization. It is also possible to combine an ensemble of low resolution
models to obtain a better prediction. However, these approaches fail to capture
the redundant information and patterns in the low-resolution input that could
help improve the quality of prediction. In this paper, we propose and evaluate
a strategy based on a deep neural network to learn a high-resolution
representation from low-resolution predictions using weather forecast as a
practical use case. We take a supervised learning approach, since obtaining
labeled data can be done automatically. Our results show significant
improvement when compared with standard practices and the strategy is still
lightweight enough to run on modest computer systems.
LSTM-Based Goal Recognition in Latent Space
Authors: Leonardo Amado, João Paulo Aires, Ramon Fraga Pereira, Maurício C. Magnaguagno, Roger Granada, Felipe Meneguzzi
Date: 2018-08-15 00:00:00
Tasks: General Classification

Approaches to goal recognition have progressively relaxed the requirements
about the amount of domain knowledge and available observations, yielding
accurate and efficient algorithms capable of recognizing goals. However, to
recognize goals in raw data, recent approaches require either human engineered
domain knowledge, or samples of behavior that account for almost all actions
being observed to infer possible goals. This is clearly too strong a
requirement for real-world applications of goal recognition, and we develop an
approach that leverages advances in recurrent neural networks to perform goal
recognition as a classification task, using encoded plan traces for training.
We empirically evaluate our approach against the state-of-the-art in goal
recognition with image-based domains, and discuss under which conditions our
approach is superior to previous ones.
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks
Authors: Penghang Yin, Shuai Zhang, Jiancheng Lyu, Stanley Osher, Yingyong Qi, Jack Xin
Date: 2018-08-15 00:00:00
Tasks: Binarization, Quantization

Quantized deep neural networks (QDNNs) are attractive due to their much lower
memory storage and faster inference speed than their regular full precision
counterparts. To maintain the same performance level especially at low
bit-widths, QDNNs must be retrained. Their training involves piecewise constant
activation functions and discrete weights, hence mathematical challenges arise.
We introduce the notion of coarse gradient and propose the blended coarse
gradient descent (BCGD) algorithm, for training fully quantized neural
networks. Coarse gradient is generally not a gradient of any function but an
artificial ascent direction. The weight update of BCGD goes by coarse gradient
correction of a weighted average of the full precision weights and their
quantization (the so-called blending), which yields sufficient descent in the
objective value and thus accelerates the training. Our experiments demonstrate
that this simple blending technique is very effective for quantization at
extremely low bit-width such as binarization. In full quantization of ResNet-18
for ImageNet classification task, BCGD gives 64.36\% top-1 accuracy with binary
weights across all layers and 4-bit adaptive activation. If the weights in the
first and last layers are kept in full precision, this number increases to
65.46\%. As theoretical justification, we show convergence analysis of coarse
gradient descent for a two-linear-layer neural network model with Gaussian
input data, and prove that the expected coarse gradient correlates positively
with the underlying true gradient.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Authors: Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong liu, Zhen Qian, Nan Du, Wei Fan, Xiaohui Xie
Date: 2018-08-15 00:00:00
Tasks: 3D Medical Imaging Segmentation, Anatomy, Medical Image Segmentation

Methods: Our deep learning model, called AnatomyNet, segments OARs from head
and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT
images as input and generating masks of all OARs of interest in one shot.
AnatomyNet is built upon the popular 3D U-net architecture, but extends it in
three important ways: 1) a new encoding scheme to allow auto-segmentation on
whole-volume CT images instead of local patches or subsets of slices, 2)
incorporating 3D squeeze-and-excitation residual blocks in encoding layers for
better feature representation, and 3) a new loss function combining Dice scores
and focal loss to facilitate the training of the neural model. These features
are designed to address two main challenges in deep-learning-based HaN
segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic
nerves) occupying only a few slices, and b) training with inconsistent data
annotations with missing ground truth for some anatomical structures.
  Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI
Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to
evaluate the performance of AnatomyNet. The objective is to segment nine
anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right,
parotid gland left, parotid gland right, submandibular gland left, and
submandibular gland right. Compared to previous state-of-the-art results from
the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient
by 3.3% on average. AnatomyNet takes about 0.12 seconds to fully segment a head
and neck CT image of dimension 178 x 302 x 225, significantly faster than
previous methods. In addition, the model is able to process whole-volume CT
images and delineate all OARs in one pass, requiring little pre- or
post-processing.
https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation.git.
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams
Authors: Lukas Cavigelli, Luca Benini
Date: 2018-08-15 00:00:00
Tasks: object-detection, Object Detection, Semantic Segmentation

The last few years have brought advances in computer vision at an amazing
pace, grounded on new findings in deep neural network construction and training
as well as the availability of large labeled datasets. Applying these networks
to images demands a high computational effort and pushes the use of
state-of-the-art networks on real-time video data out of reach of embedded
platforms. Many recent works focus on reducing network complexity for real-time
inference on embedded computing platforms. We adopt an orthogonal viewpoint and
propose a novel algorithm exploiting the spatio-temporal sparsity of pixel
changes. This optimized inference procedure resulted in an average speed-up of
9.1x over cuDNN on the Tegra X2 platform at a negligible accuracy loss of <0.1%
and no retraining of the network for a semantic segmentation application.
Similarly, an average speed-up of 7.0x has been achieved for a pose detection
DNN and a reduction of 5x of the number of arithmetic operations to be
performed for object detection on static camera video surveillance data. These
throughput gains combined with a lower power consumption result in an energy
efficiency of 511 GOp/s/W compared to 70 GOp/s/W for the baseline.
A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system
Authors: Arabin Kumar Dey, Himanshu Jhamb
Date: 2018-08-15 00:00:00
Tasks: Movie Recommendation

In this article we select the unknown dimension of the feature by re-
versible jump MCMC inside a simulated annealing in bayesian set up of
collaborative filter. We implement the same in MovieLens small dataset. We also
tune the hyper parameter by using a modified empirical bayes. It can also be
used to guess an initial choice for hyper-parameters in grid search procedure
even for the datasets where MCMC oscillates around the true value or takes long
time to converge.
Cross-view image synthesis using geometry-guided conditional GANs
Authors: Krishna Regmi, Ali Borji
Date: 2018-08-14 00:00:00
Tasks: Cross-View Image-to-Image Translation, Image Generation

We address the problem of generating images across two drastically different views, namely ground (street) and aerial (overhead) views. Image synthesis by itself is a very challenging computer vision task and is even more so when generation is conditioned on an image in another view. Due the difference in viewpoints, there is small overlapping field of view and little common content between these two views. Here, we try to preserve the pixel information between the views so that the generated image is a realistic representation of cross view input image. For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image. We then use generative adversarial networks to inpaint the missing regions in the transformed image and add realism to it. Our exhaustive evaluation and model comparison demonstrate that utilizing geometry constraints adds fine details to the generated images and can be a better approach for cross view image synthesis than purely pixel based synthesis methods.
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images
Authors: Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, Volker Lindenstruth, Gerhard Hummer
Date: 2016-09-21 00:00:00
Tasks: Bayesian Inference, CPU, Electron Tomography, GPU

In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculated for individual images, BioEM in principle addresses the challenge of
working with highly dynamic or heterogeneous systems not easily handled in
traditional EM reconstruction. However, the calculation of these posteriors for
large numbers of particles and models is computationally demanding. Here we
present highly parallelized, GPU-accelerated computer software that performs
this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI
parallelization combined with both CPU and GPU computing. The resulting BioEM
software scales nearly ideally both on pure CPU and on CPU+GPU architectures,
thus enabling Bayesian analysis of tens of thousands of images in a reasonable
time. The general mathematical framework and robust algorithms are not limited
to cryo-electron microscopy but can be generalized for electron tomography and
other imaging experiments.
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach
Authors: He He, Dongrui Wu
Date: 2018-08-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Motor Imagery, Transfer Learning

Objective: This paper targets a major challenge in developing practical
EEG-based brain-computer interfaces (BCIs): how to cope with individual
differences so that better learning performance can be obtained for a new
subject, with minimum or even no subject-specific data? Methods: We propose a
novel approach to align EEG trials from different subjects in the Euclidean
space to make them more similar, and hence improve the learning performance for
a new subject. Our approach has three desirable properties: 1) it aligns the
EEG trials directly in the Euclidean space, and any signal processing, feature
extraction and machine learning algorithms can then be applied to the aligned
trials; 2) its computational cost is very low; and, 3) it is unsupervised and
does not need any label information from the new subject. Results: Both offline
and simulated online experiments on motor imagery classification and
event-related potential classification verified that our proposed approach
outperformed a state-of-the-art Riemannian space data alignment approach, and
several approaches without data alignment. Conclusion: The proposed Euclidean
space EEG data alignment approach can greatly facilitate transfer learning in
BCIs. Significance: Our proposed approach is effective, efficient, and easy to
implement. It could be an essential pre-processing step for EEG-based BCIs.