pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Photoacoustic Tomography from Sparse Data
Authors: Stephan Antholzer, Markus Haltmeier, Johannes Schwab
Date: 2017-04-15 00:00:00
Tasks: Deep Learning, Image Reconstruction

The development of fast and accurate image reconstruction algorithms is a
central aspect of computed tomography. In this paper, we investigate this issue
for the sparse data problem in photoacoustic tomography (PAT). We develop a
direct and highly efficient reconstruction algorithm based on deep learning. In
our approach image reconstruction is performed with a deep convolutional neural
network (CNN), whose weights are adjusted prior to the actual image
reconstruction based on a set of training data. The proposed reconstruction
approach can be interpreted as a network that uses the PAT filtered
backprojection algorithm for the first layer, followed by the U-net
architecture for the remaining layers. Actual image reconstruction with deep
learning consists in one evaluation of the trained CNN, which does not require
time consuming solution of the forward and adjoint problems. At the same time,
our numerical results demonstrate that the proposed deep learning approach
reconstructs images with a quality comparable to state of the art iterative
approaches for PAT from sparse data.
Syllable-level Neural Language Model for Agglutinative Language
Authors: Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Date: 2017-08-18 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, model

Language models for agglutinative languages have always been hindered in past
due to myriad of agglutinations possible to any given word through various
affixes. We propose a method to diminish the problem of out-of-vocabulary words
by introducing an embedding derived from syllables and morphemes which
leverages the agglutinative property. Our model outperforms character-level
embedding in perplexity by 16.87 with 9.50M parameters. Proposed method
achieves state of the art performance over existing input prediction methods in
terms of Key Stroke Saving and has been commercialized.
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
Authors: Weimin Wang, Ken Sakurada, Nobuo Kawaguchi
Date: 2017-08-18 00:00:00

This paper presents a novel method for fully automatic and convenient
extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally
printed chessboard. The proposed method is based on the 3D corner estimation of
the chessboard from the sparse point cloud generated by one frame scan of the
LiDAR. To estimate the corners, we formulate a full-scale model of the
chessboard and fit it to the segmented 3D points of the chessboard. The model
is fitted by optimizing the cost function under constraints of correlation
between the reflectance intensity of laser and the color of the chessboard's
patterns. Powell's method is introduced for resolving the discontinuity problem
in optimization. The corners of the fitted model are considered as the 3D
corners of the chessboard. Once the corners of the chessboard in the 3D point
cloud are estimated, the extrinsic calibration of the two sensors is converted
to a 3D-2D matching problem. The corresponding 3D-2D points are used to
calculate the absolute pose of the two sensors with Unified Perspective-n-Point
(UPnP). Further, the calculated parameters are regarded as initial values and
are refined using the Levenberg-Marquardt method. The performance of the
proposed corner detection method from the 3D point cloud is evaluated using
simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR
and a Ladybug3 camera under the proposed re-projection error metric,
qualitatively and quantitatively demonstrate the accuracy and stability of the
final extrinsic calibration parameters.
Training Group Orthogonal Neural Networks with Privileged Information
Authors: Yunpeng Chen, Xiaojie Jin, Jiashi Feng, Shuicheng Yan
Date: 2017-01-24 00:00:00
Tasks: Diversity, image-classification, Image Classification, Image Segmentation, Semantic Segmentation

Learning rich and diverse representations is critical for the performance of
deep convolutional neural networks (CNNs). In this paper, we consider how to
use privileged information to promote inherent diversity of a single CNN model
such that the model can learn better representations and offer stronger
generalization ability. To this end, we propose a novel group orthogonal
convolutional neural network (GoCNN) that learns untangled representations
within each layer by exploiting provided privileged information and enhances
representation diversity effectively. We take image classification as an
example where image segmentation annotations are used as privileged information
during the training process. Experiments on two benchmark datasets -- ImageNet
and PASCAL VOC -- clearly demonstrate the strong generalization ability of our
proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance
of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses
privileged information of 10% of the training images, confirming effectiveness
of GoCNN on utilizing available privileged knowledge to train better CNNs.
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification
Authors: Sanping Zhou, Jinjun Wang, Rui Shi, Qiqi Hou, Yihong Gong, Nanning Zheng
Date: 2017-08-18 00:00:00
Tasks: Person Re-Identification, Retrieval

Person re-identification (Re-ID) aims at matching images of the same person
across disjoint camera views, which is a challenging problem in multimedia
analysis, multimedia editing and content-based media retrieval communities. The
major challenge lies in how to preserve similarity of the same person across
video footages with large appearance variations, while discriminating different
individuals. To address this problem, conventional methods usually consider the
pairwise similarity between persons by only measuring the point to point (P2P)
distance. In this paper, we propose to use deep learning technique to model a
novel set to set (S2S) distance, in which the underline objective focuses on
preserving the compactness of intra-class samples for each camera view, while
maximizing the margin between the intra-class set and inter-class set. The S2S
distance metric is consisted of three terms, namely the class-identity term,
the relative distance term and the regularization term. The class-identity term
keeps the intra-class samples within each camera view gathering together, the
relative distance term maximizes the distance between the intra-class class set
and inter-class set across different camera views, and the regularization term
smoothness the parameters of deep convolutional neural network (CNN). As a
result, the final learned deep model can effectively find out the matched
target to the probe object among various candidates in the video gallery by
learning discriminative and stable feature representations. Using the CUHK01,
CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted
comparative evaluations to demonstrate the advantages of our method over the
state-of-the-art approaches.
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
Authors: Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, Zhihao Fang
Date: 2017-08-18 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Automatic generation of facial images has been well studied after the
Generative Adversarial Network (GAN) came out. There exists some attempts
applying the GAN model to the problem of generating facial images of anime
characters, but none of the existing work gives a promising result. In this
work, we explore the training of GAN models specialized on an anime facial
image dataset. We address the issue from both the data and the model aspect, by
collecting a more clean, well-suited dataset and leverage proper, empirical
application of DRAGAN. With quantitative analysis and case studies we
demonstrate that our efforts lead to a stable and high-quality model. Moreover,
to assist people with anime character design, we build a website
(http://make.girls.moe) with our pre-trained model available online, which
makes the model easily accessible to general public.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Authors: Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao
Date: 2017-08-18 00:00:00

Deep neural networks (DNNs) have demonstrated impressive performance on a
wide array of tasks, but they are usually considered opaque since internal
structure and learned parameters are not interpretable. In this paper, we
re-examine the internal representations of DNNs using adversarial images, which
are generated by an ensemble-optimization algorithm. We find that: (1) the
neurons in DNNs do not truly detect semantic objects/parts, but respond to
objects/parts only as recurrent discriminative patches; (2) deep visual
representations are not robust distributed codes of visual concepts because the
representations of adversarial images are largely not consistent with those of
real images, although they have similar visual appearance, both of which are
different from previous findings. To further improve the interpretability of
DNNs, we propose an adversarial training scheme with a consistent loss such
that the neurons are endowed with human-interpretable concepts. The induced
interpretable representations enable us to trace eventual outcomes back to
influential neurons. Therefore, human users can know how the models make
predictions, as well as when and why they make errors.
Debiased distributed learning for sparse partial linear models in high dimensions
Authors: Shaogao Lv, Heng Lian
Date: 2017-08-18 00:00:00
Tasks: Distributed Optimization, Vocal Bursts Intensity Prediction

Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distributed optimization for semi-paramemetric models with multiple-level structures (e.g. sparsity, linearity and nonlinearity). To address these issues, the current paper proposes a new communication-efficient distributed learning algorithm for partially sparse linear models with an increasing number of features. The proposed method is based on the classical divide and conquer strategy for handing big data and each sub-method defined on each subsample consists of a debiased estimation of the double-regularized least squares approach. With the proposed method, we theoretically prove that our global parametric estimator can achieve optimal parametric rate in our semi-parametric model given an appropriate partition on the total data. Specially, the choice of data partition relies on the underlying smoothness of the nonparametric component, but it is adaptive to the sparsity parameter. Even under the non-distributed setting, we develop a new and easily-read proof for optimal estimation of the parametric error in high dimensional partial linear model. Finally, several simulated experiments are implemented to indicate comparable empirical performance of our debiased technique under the distributed setting.
Temporal Generative Adversarial Nets with Singular Value Clipping
Authors: Masaki Saito, Eiichi Matsumoto, Shunta Saito
Date: 2016-11-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Video Generation

In this paper, we propose a generative model, Temporal Generative Adversarial
Nets (TGAN), which can learn a semantic representation of unlabeled videos, and
is capable of generating videos. Unlike existing Generative Adversarial Nets
(GAN)-based methods that generate videos with a single generator consisting of
3D deconvolutional layers, our model exploits two different types of
generators: a temporal generator and an image generator. The temporal generator
takes a single latent variable as input and outputs a set of latent variables,
each of which corresponds to an image frame in a video. The image generator
transforms a set of such latent variables into a video. To deal with
instability in training of GAN with such advanced networks, we adopt a recently
proposed model, Wasserstein GAN, and propose a novel method to train it stably
in an end-to-end manner. The experimental results demonstrate the effectiveness
of our methods.
Predicting Complete 3D Models of Indoor Scenes
Authors: Ruiqi Guo, Chuhang Zou, Derek Hoiem
Date: 2015-04-09 00:00:00
Tasks: Diversity, Visual Reasoning

One major goal of vision is to infer physical models of objects, surfaces,
and their layout from sensors. In this paper, we aim to interpret indoor scenes
from one RGBD image. Our representation encodes the layout of walls, which must
conform to a Manhattan structure but is otherwise flexible, and the layout and
extent of objects, modeled with CAD-like 3D shapes. We represent both the
visible and occluded portions of the scene, producing a complete 3D parse. Such
a scene interpretation is useful for robotics and visual reasoning, but
difficult to produce due to the well-known challenge of segmentation, the high
degree of occlusion, and the diversity of objects in indoor scene. We take a
data-driven approach, generating sets of potential object regions, matching to
regions in training images, and transferring and aligning associated 3D models
while encouraging fit to observations and overall consistency. We demonstrate
encouraging results on the NYU v2 dataset and highlight a variety of
interesting directions for future work.
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
Authors: Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How
Date: 2017-07-24 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.
Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii
Authors: Mojtaba Sedigh Fazli, Stephen Andrew Vella, Silvia N. J. Moreno, Shannon Quinn
Date: 2017-08-01 00:00:00
Tasks: Optical Flow Estimation

Toxoplasma gondii is the causative agent responsible for toxoplasmosis and
serves as one of the most common parasites in the world. For a successful lytic
cycle, T. gondii must traverse biological barriers in order to invade host
cells, and as such, motility is critical for its virulence. Calcium signaling,
governed by fluctuations in cytosolic calcium (Ca2+) concentrations, is
utilized universally across life and regulates many cellular processes,
including the stimulation of T. gondii virulence factors such as motility.
Therefore, increases in cytosolic calcium, called calcium oscillations, serve
as a means to link and quantify the intracellular signaling processes that lead
to T. gondii motility and invasion. Here, we describe our work extracting,
quantifying and modeling motility patterns of T. gondii before and after the
addition of pharmacological drugs and/or extracellular calcium. We demonstrate
a computational pipeline including a robust tracking system using optical flow
and dense trajectory features to extract T. gondii motility patterns. Using
this pipeline, we were able to track changes in T.gondii motility in response
to cytosolic Ca2+ fluxes in extracellular parasites. This allows us to study
how Ca2+ signaling via release from intracellular Ca2+ stores and/or from
extracellular Ca2+ entry relates to motility patterns, a crucial first step in
developing countermeasures for T. gondii virulence.
Consistency of Dirichlet Partitions
Authors: Braxton Osting, Todd Harry Reeb
Date: 2017-08-18 00:00:00

A Dirichlet $k$-partition of a domain $U \subseteq \mathbb{R}^d$ is a
collection of $k$ pairwise disjoint open subsets such that the sum of their
first Laplace-Dirichlet eigenvalues is minimal. A discrete version of Dirichlet
partitions has been posed on graphs with applications in data analysis. Both
versions admit variational formulations: solutions are characterized by
minimizers of the Dirichlet energy of mappings from $U$ into a singular space
$\Sigma_k \subseteq \mathbb{R}^k$. In this paper, we extend results of N.\
Garc\'ia Trillos and D.\ Slep\v{c}ev to show that there exist solutions of the
continuum problem arising as limits to solutions of a sequence of discrete
problems. Specifically, a sequence of points $\{x_i\}_{i \in \mathbb{N}}$ from
$U$ is sampled i.i.d.\ with respect to a given probability measure $\nu$ on $U$
and for all $n \in \mathbb{N}$, a geometric graph $G_n$ is constructed from the
first $n$ points $x_1, x_2, \ldots, x_n$ and the pairwise distances between the
points. With probability one with respect to the choice of points $\{x_i\}_{i
\in \mathbb{N}}$, we show that as $n \to \infty$ the discrete Dirichlet
energies for functions $G_n \to \Sigma_k$ $\Gamma$-converge to (a scalar
multiple of) the continuum Dirichlet energy for functions $U \to \Sigma_k$ with
respect to a metric coming from the theory of optimal transport. This, along
with a compactness property for the aforementioned energies that we prove,
implies the convergence of minimizers. When $\nu$ is the uniform distribution,
our results also imply the statistical consistency statement that Dirichlet
partitions of geometric graphs converge to partitions of the sampled space in
the Hausdorff sense.
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
Authors: Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George
Date: 2017-06-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Reinforcement Learning, Zero-shot Generalization

The recent adaptation of deep neural network-based methods to reinforcement
learning and planning domains has yielded remarkable progress on individual
tasks. Nonetheless, progress on task-to-task transfer remains limited. In
pursuit of efficient and robust generalization, we introduce the Schema
Network, an object-oriented generative physics simulator capable of
disentangling multiple causes of events and reasoning backward through causes
to achieve goals. The richly structured architecture of the Schema Network can
learn the dynamics of an environment directly from data. We compare Schema
Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a
suite of Breakout variations, reporting results on training efficiency and
zero-shot generalization, consistently demonstrating faster, more robust
learning and better transfer. We argue that generalizing from limited data and
learning causal relationships are essential abilities on the path toward
generally intelligent systems.
Large-Scale Domain Adaptation via Teacher-Student Learning
Authors: Jinyu Li, Michael L. Seltzer, Xi Wang, Rui Zhao, Yifan Gong
Date: 2017-08-17 00:00:00
Tasks: Domain Adaptation, speech-recognition, Speech Recognition

High accuracy speech recognition requires a large amount of transcribed data
for supervised training. In the absence of such data, domain adaptation of a
well-trained acoustic model can be performed, but even here, high accuracy
usually requires significant labeled data from the target domain. In this work,
we propose an approach to domain adaptation that does not require
transcriptions but instead uses a corpus of unlabeled parallel data, consisting
of pairs of samples from the source domain of the well-trained model and the
desired target domain. To perform adaptation, we employ teacher/student (T/S)
learning, in which the posterior probabilities generated by the source-domain
model can be used in lieu of labels to train the target-domain model. We
evaluate the proposed approach in two scenarios, adapting a clean acoustic
model to noisy speech and adapting an adults speech acoustic model to children
speech. Significant improvements in accuracy are obtained, with reductions in
word error rate of up to 44% over the original source model without the need
for transcribed data in the target domain. Moreover, we show that increasing
the amount of unlabeled data results in additional model robustness, which is
particularly beneficial when using simulated training data in the
target-domain.
Eigen Evolution Pooling for Human Action Recognition
Authors: Yang Wang, Vinh Tran, Minh Hoai
Date: 2017-08-17 00:00:00
Tasks: Action Recognition, Temporal Action Localization

We introduce Eigen Evolution Pooling, an efficient method to aggregate a
sequence of feature vectors. Eigen evolution pooling is designed to produce
compact feature representations for a sequence of feature vectors, while
maximally preserving as much information about the sequence as possible,
especially the temporal evolution of the features over time. Eigen evolution
pooling is a general pooling method that can be applied to any sequence of
feature vectors, from low-level RGB values to high-level Convolutional Neural
Network (CNN) feature vectors. We show that eigen evolution pooling is more
effective than average, max, and rank pooling for encoding the dynamics of
human actions in video. We demonstrate the power of eigen evolution pooling on
UCF101 and Hollywood2 datasets, two human action recognition benchmarks, and
achieve state-of-the-art performance.
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning
Authors: Dustin Morley, Hassan Foroosh, Saad Shaikh, Ulas Bagci
Date: 2017-08-17 00:00:00
Tasks: Data Augmentation, Decoder, Deep Learning, Segmentation

We propose a new deep learning approach for automatic detection and
segmentation of fluid within retinal OCT images. The proposed framework
utilizes both ResNet and Encoder-Decoder neural network architectures. When
training the network, we apply a novel data augmentation method called myopic
warping together with standard rotation-based augmentation to increase the
training set size to 45 times the original amount. Finally, the network output
is post-processed with an energy minimization algorithm (graph cut) along with
a few other knowledge guided morphological operations to finalize the
segmentation process. Based on OCT imaging data and its ground truth from the
RETOUCH challenge, the proposed system achieves dice indices of 0.522, 0.682,
and 0.612, and average absolute volume differences of 0.285, 0.115, and 0.156
mm$^3$ for intaretinal fluid, subretinal fluid, and pigment epithelial
detachment respectively.