pwc-archive/papers-with-abstracts huggingface dataset:
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
Authors: Chi Zhang, Yixin Zhu, Song-Chun Zhu
Date: 2018-12-13 00:00:00
Tasks: Bilevel Optimization, Style Transfer

An unprecedented booming has been witnessed in the research area of artistic
style transfer ever since Gatys et al. introduced the neural method. One of the
remaining challenges is to balance a trade-off among three critical
aspects---speed, flexibility, and quality: (i) the vanilla optimization-based
algorithm produces impressive results for arbitrary styles, but is
unsatisfyingly slow due to its iterative nature, (ii) the fast approximation
methods based on feed-forward neural networks generate satisfactory artistic
effects but bound to only a limited number of styles, and (iii)
feature-matching methods like AdaIN achieve arbitrary style transfer in a
real-time manner but at a cost of the compromised quality. We find it
considerably difficult to balance the trade-off well merely using a single
feed-forward step and ask, instead, whether there exists an algorithm that
could adapt quickly to any style, while the adapted model maintains high
efficiency and good image quality. Motivated by this idea, we propose a novel
method, coined MetaStyle, which formulates the neural style transfer as a
bilevel optimization problem and combines learning with only a few
post-processing update steps to adapt to a fast approximation model with
satisfying artistic effects, comparable to the optimization-based methods for
an arbitrary style. The qualitative and quantitative analysis in the
experiments demonstrates that the proposed approach achieves high-quality
arbitrary artistic style transfer effectively, with a good trade-off among
speed, flexibility, and quality.
Nrityantar: Pose oblivious Indian classical dance sequence classification system
Authors: Vinay Kaushik, Prerana Mukherjee, Brejesh lall
Date: 2018-12-13 00:00:00
Tasks: Action Recognition, Classification, General Classification, Pose Estimation, Temporal Action Localization

In this paper, we attempt to advance the research work done in human action
recognition to a rather specialized application namely Indian Classical Dance
(ICD) classification. The variation in such dance forms in terms of hand and
body postures, facial expressions or emotions and head orientation makes pose
estimation an extremely challenging task. To circumvent this problem, we
construct a pose-oblivious shape signature which is fed to a sequence learning
framework. The pose signature representation is done in two-fold process.
First, we represent person-pose in first frame of a dance video using symmetric
Spatial Transformer Networks (STN) to extract good person object proposals and
CNN-based parallel single person pose estimator (SPPE). Next, the pose basis
are converted to pose flows by assigning a similarity score between successive
poses followed by non-maximal suppression. Instead of feeding a simple chain of
joints in the sequence learner which generally hinders the network performance
we constitute a feature vector of the normalized distance vectors, flow, angles
between anchor joints which captures the adjacency configuration in the
skeletal pattern. Thus, the kinematic relationship amongst the body joints
across the frames using pose estimation helps in better establishing the
spatio-temporal dependencies. We present an exhaustive empirical evaluation of
state-of-the-art deep network based methods for dance classification on ICD
dataset.
Hierarchical Bipartite Graph Convolution Networks
Authors: Marcel Nassar
Date: 2018-11-17 00:00:00
Tasks: Graph Neural Network

Recently, graph neural networks have been adopted in a wide variety of
applications ranging from relational representations to modeling irregular data
domains such as point clouds and social graphs. However, the space of graph
neural network architectures remains highly fragmented impeding the development
of optimized implementations similar to what is available for convolutional
neural networks. In this work, we present BiGraphNet, a graph neural network
architecture that generalizes many popular graph neural network models and
enables new efficient operations similar to those supported by ConvNets. By
explicitly separating the input and output nodes, BiGraphNet: (i) generalizes
the graph convolution to support new efficient operations such as coarsened
graph convolutions (similar to strided convolution in convnets), multiple input
graphs convolution and graph expansions (unpooling) which can be used to
implement various graph architectures such as graph autoencoders, and graph
residual nets; and (ii) accelerates and scales the computations and memory
requirements in hierarchical networks by performing computations only at
specified output nodes.
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Authors: Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek, Inkyu Lee
Date: 2018-12-13 00:00:00

Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.
Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes
Authors: Yunyi Li, Tong Wang
Date: 2018-12-13 00:00:00

Our goal is to predict the location of the next crime in a crime series,
based on the identified previous offenses in the series. We build a predictive
model called Next Hit Predictor (NHP) that finds the most likely location of
the next serial crime via a carefully designed risk model. The risk model
follows the paradigm of a self-exciting point process which consists of a
background crime risk and triggered risks stimulated by previous offenses in
the series. Thus, NHP creates a risk map for a crime series at hand. To train
the risk model, we formulate a convex learning objective that considers
pairwise rankings of locations and use stochastic gradient descent to learn the
optimal parameters. Next Hit Predictor incorporates both spatial-temporal
features and geographical characteristics of prior crime locations in the
series. Next Hit Predictor has demonstrated promising results on decades' worth
of serial crime data collected by the Crime Analysis Unit of the Cambridge
Police Department in Massachusetts, USA.
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
Authors: Chengsheng Mao, Lijuan Lu, Bin Hu
Date: 2018-12-13 00:00:00
Tasks: Classification, General Classification, model

In Bayesian classification, it is important to establish a probabilistic
model for each class for likelihood estimation. Most of the previous methods
modeled the probability distribution in the whole sample space. However,
real-world problems are usually too complex to model in the whole sample space;
some fundamental assumptions are required to simplify the global model, for
example, the class conditional independence assumption for naive Bayesian
classification. In this paper, with the insight that the distribution in a
local sample space should be simpler than that in the whole sample space, a
local probabilistic model established for a local region is expected much
simpler and can relax the fundamental assumptions that may not be true in the
whole sample space. Based on these advantages we propose establishing local
probabilistic models for Bayesian classification. In addition, a Bayesian
classifier adopting a local probabilistic model can even be viewed as a
generalized local classification model; by tuning the size of the local region
and the corresponding local model assumption, a fitting model can be
established for a particular classification problem. The experimental results
on several real-world datasets demonstrate the effectiveness of local
probabilistic models for Bayesian classification.
Advances of Scene Text Datasets
Authors: Masakazu Iwamura
Date: 2018-12-13 00:00:00
Tasks: Scene Text Detection, Text Detection

This article introduces publicly available datasets in scene text detection
and recognition. The information is as of 2017.
Tight Analyses for Non-Smooth Stochastic Gradient Descent
Authors: Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa
Date: 2018-12-13 00:00:00

Consider the problem of minimizing functions that are Lipschitz and strongly
convex, but not necessarily differentiable. We prove that after $T$ steps of
stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$
with high probability. We also construct a function from this class for which
the error of the final iterate of deterministic gradient descent is
$\Omega(\log(T)/T)$. This shows that the upper bound is tight and that, in this
setting, the last iterate of stochastic gradient descent has the same general
error rate (with high probability) as deterministic gradient descent. This
resolves both open questions posed by Shamir (2012).
  An intermediate step of our analysis proves that the suffix averaging method
achieves error $O(1/T)$ with high probability, which is optimal (for any
first-order optimization method). This improves results of Rakhlin (2012) and
Hazan and Kale (2014), both of which achieved error $O(1/T)$, but only in
expectation, and achieved a high probability error bound of $O(\log
\log(T)/T)$, which is suboptimal.
  We prove analogous results for functions that are Lipschitz and convex, but
not necessarily strongly convex or differentiable. After $T$ steps of
stochastic gradient descent, the error of the final iterate is
$O(\log(T)/\sqrt{T})$ with high probability, and there exists a function for
which the error of the final iterate of deterministic gradient descent is
$\Omega(\log(T)/\sqrt{T})$.
Learning to Learn from Noisy Labeled Data
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification, Learning with noisy labels, Meta-Learning

Despite the success of deep neural networks (DNNs) in image classification
tasks, the human-level performance relies on massive training data with
high-quality manual annotations, which are expensive and time-consuming to
collect. There exist many inexpensive data sources on the web, but they tend to
contain inaccurate labels. Training on noisy labeled datasets causes
performance degradation because DNNs can easily overfit to the label noise. To
overcome this problem, we propose a noise-tolerant training algorithm, where a
meta-learning update is performed prior to conventional gradient update. The
proposed meta-learning method simulates actual training by generating synthetic
noisy labels, and train the model such that after one gradient update using
each set of synthetic noisy labels, the model does not overfit to the specific
noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the
Clothing1M dataset. The results demonstrate the advantageous performance of the
proposed method compared to several state-of-the-art baselines.
Conditional Graph Neural Processes: A Functional Autoencoder Approach
Authors: Marcel Nassar, Xin Wang, Evren Tumer
Date: 2018-12-13 00:00:00
Tasks: Decoder

We introduce a novel encoder-decoder architecture to embed functional
processes into latent vector spaces. This embedding can then be decoded to
sample the encoded functions over any arbitrary domain. This autoencoder
generalizes the recently introduced Conditional Neural Process (CNP) model of
random processes. Our architecture employs the latest advances in graph neural
networks to process irregularly sampled functions. Thus, we refer to our model
as Conditional Graph Neural Process (CGNP). Graph neural networks can
effectively exploit `local' structures of the metric spaces over which the
functions/processes are defined. The contributions of this paper are twofold:
(i) a novel graph-based encoder-decoder architecture for functional and process
embeddings, and (ii) a demonstration of the importance of using the structure
of metric spaces for this type of representations.
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Authors: Ye Wang, Jongmoo Choi, Yueru Chen, Qin Huang, Siyang Li, Ming-Sui Lee, C. -C. Jay Kuo
Date: 2018-12-13 00:00:00
Tasks: Instance Segmentation, Object, Object Tracking, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

One major technique debt in video object segmentation is to label the object
masks for training instances. As a result, we propose to prepare inexpensive,
yet high quality pseudo ground truth corrected with motion cue for video object
segmentation training. Our method conducts semantic segmentation using instance
segmentation networks and, then, selects the segmented object of interest as
the pseudo ground truth based on the motion information. Afterwards, the pseudo
ground truth is exploited to finetune the pretrained objectness network to
facilitate object segmentation in the remaining frames of the video. We show
that the pseudo ground truth could effectively improve the segmentation
performance. This straightforward unsupervised video object segmentation method
is more efficient than existing methods. Experimental results on DAVIS and FBMS
show that the proposed method outperforms state-of-the-art unsupervised
segmentation methods on various benchmark datasets. And the category-agnostic
pseudo ground truth has great potential to extend to multiple arbitrary object
tracking.
Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling Task
Authors: Liang Qiu, Yuanyi Ding, Lei He
Date: 2018-12-12 00:00:00
Tasks: Language Modeling, Language Modelling, slot-filling, Slot Filling, Spoken Language Understanding

In recent years, Recurrent Neural Networks (RNNs) based models have been
applied to the Slot Filling problem of Spoken Language Understanding and
achieved the state-of-the-art performances. In this paper, we investigate the
effect of incorporating pre-trained language models into RNN based Slot Filling
models. Our evaluation on the Airline Travel Information System (ATIS) data
corpus shows that we can significantly reduce the size of labeled training data
and achieve the same level of Slot Filling performance by incorporating extra
word embedding and language model embedding layers pre-trained on unlabeled
corpora.
Massively scalable Sinkhorn distances via the Nyström method
Authors: Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed
Date: 2018-12-12 00:00:00
Proceeding: NeurIPS 2019 12

The Sinkhorn "distance", a variant of the Wasserstein distance with entropic regularization, is an increasingly popular tool in machine learning and statistical inference. However, the time and memory requirements of standard algorithms for computing this distance grow quadratically with the size of the data, making them prohibitively expensive on massive data sets. In this work, we show that this challenge is surprisingly easy to circumvent: combining two simple techniques---the Nystr\"om method and Sinkhorn scaling---provably yields an accurate approximation of the Sinkhorn distance with significantly lower time and memory requirements than other approaches. We prove our results via new, explicit analyses of the Nystr\"om method and of the stability properties of Sinkhorn scaling. We validate our claims experimentally by showing that our approach easily computes Sinkhorn distances on data sets hundreds of times larger than can be handled by other techniques.
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
Authors: Mohammed A. Aabed, Gukyeong Kwon, Ghassan AlRegib
Date: 2018-12-12 00:00:00
Tasks: Video Quality Assessment

We propose a perceptual video quality assessment (PVQA) metric for distorted
videos by analyzing the power spectral density (PSD) of a group of pictures.
This is an estimation approach that relies on the changes in video dynamic
calculated in the frequency domain and are primarily caused by distortion. We
obtain a feature map by processing a 3D PSD tensor obtained from a set of
distorted frames. This is a full-reference tempospatial approach that considers
both temporal and spatial PSD characteristics. This makes it ubiquitously
suitable for videos with varying motion patterns and spatial contents. Our
technique does not make any assumptions on the coding conditions, streaming
conditions or distortion. This approach is also computationally inexpensive
which makes it feasible for real-time and practical implementations. We
validate our proposed metric by testing it on a variety of distorted sequences
from PVQA databases. The results show that our metric estimates the perceptual
quality at the sequence level accurately. We report the correlation
coefficients with the differential mean opinion scores (DMOS) reported in the
databases. The results show high and competitive correlations compared with the
state of the art techniques.
On Distributed Multi-player Multiarmed Bandit Problems in Abruptly Changing Environment
Authors: Lai Wei, Vaibhav Srivastava
Date: 2018-12-12 00:00:00

We study the multi-player stochastic multiarmed bandit (MAB) problem in an
abruptly changing environment. We consider a collision model in which a player
receives reward at an arm if it is the only player to select the arm. We design
two novel algorithms, namely, Round-Robin Sliding-Window Upper Confidence
Bound\# (RR-SW-UCB\#), and the Sliding-Window Distributed Learning with
Prioritization (SW-DLP). We rigorously analyze these algorithms and show that
the expected cumulative group regret for these algorithms is upper bounded by
sublinear functions of time, i.e., the time average of the regret
asymptotically converges to zero. We complement our analytic results with
numerical illustrations.
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Authors: Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon
Date: 2018-12-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
Authors: He Zhang, Benjamin S. Riggan, Shuowen Hu, Nathaniel J. Short, Vishal M. Patel
Date: 2018-12-12 00:00:00
Tasks: Decoder, Face Generation, Face Verification, Image Generation, Image Reconstruction

The large domain discrepancy between faces captured in polarimetric (or
conventional) thermal and visible domain makes cross-domain face verification a
highly challenging problem for human examiners as well as computer vision
algorithms. Previous approaches utilize either a two-step procedure (visible
feature estimation and visible image reconstruction) or an input-level fusion
technique, where different Stokes images are concatenated and used as a
multi-channel input to synthesize the visible image given the corresponding
polarimetric signatures. Although these methods have yielded improvements, we
argue that input-level fusion alone may not be sufficient to realize the full
potential of the available Stokes images. We propose a Generative Adversarial
Networks (GAN) based multi-stream feature-level fusion technique to synthesize
high-quality visible images from prolarimetric thermal images. The proposed
network consists of a generator sub-network, constructed using an
encoder-decoder network based on dense residual blocks, and a multi-scale
discriminator sub-network. The generator network is trained by optimizing an
adversarial loss in addition to a perceptual loss and an identity preserving
loss to enable photo realistic generation of visible images while preserving
discriminative characteristics. An extended dataset consisting of polarimetric
thermal facial signatures of 111 subjects is also introduced. Multiple
experiments evaluated on different experimental protocols demonstrate that the
proposed method achieves state-of-the-art performance. Code will be made
available at https://github.com/hezhangsprinter.
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
Authors: Itay Benou, Tammy Riklin-Raviv
Date: 2018-12-12 00:00:00
Tasks: Probabilistic Deep Learning, White Matter Fiber Tractography

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive performance with state-of-the art classical and machine learning based tractography algorithms. We further present qualitative results of bundle-specific probabilistic tractography obtained using our method. The code is publicly available at: https://github.com/itaybenou/DeepTract.git.